TR2021015036T - Use of follistatin in type 2 diabetes risk estimation - Google Patents
Use of follistatin in type 2 diabetes risk estimationInfo
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- TR2021015036T TR2021015036T TR2021/015036 TR2021015036T TR 2021015036 T TR2021015036 T TR 2021015036T TR 2021/015036 TR2021/015036 TR 2021/015036 TR 2021015036 T TR2021015036 T TR 2021015036T
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- diabetes
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- follistatin
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Abstract
Mevcut tarifname ile, GCKR tarafından düzenlenen karaciğer follistatin salgısı olan tip 2 diyabetin erken teşhisi ve/veya tahminine yönelik bir biyomarkör olarak follistatinin detaylı kullanımı vardır, bu kullanım burada rapor edilir. Ayrıca, bir insanda tip 2 diyabetin erken tahminine yönelik bir biyomarkör imzası oluşturmaya yönelik bir yöntem burada açıklanır.With the present disclosure, there is detailed use of follistatin as a biomarker for the early diagnosis and/or prediction of type 2 diabetes with liver follistatin secretion regulated by GCKR, a use reported here. In addition, a method for generating a biomarker signature for early prediction of type 2 diabetes in a human is described herein.
Description
TARIFNAME TIP 2 DIYABET RISK TAHMININDE FOLLISTATIN KULLANIMI BULUSUN BASLIGI Tip 2 diyabet risk tahmininde follistatin kullanimi TEKNIK SAHA Burada, HbAic, proinsülin, C-peptid dahil olmak üzere tip 2 diyabete yönelik bilinen üç kan biyomarkör içeren bir modelde bir markör olarak follistatin kullanilarak, hastalik baslangicindan dört yil öncesine kadar iyi bir tahmin degeri olan tip 2 diyabet risk degerlendirmesine yönelik yeni araçlar sunulur. DESCRIPTION USE OF FOLLISTAT IN TYPE 2 DIABETES RISK ESTIMATION TITLE OF THE INVENTION Use of follistatin in type 2 diabetes risk estimation TECHNICAL FIELD Here, three known treatments for type 2 diabetes are presented, including HbAic, proinsulin, C-peptide. disease, using follistatin as a marker in a model containing a blood biomarker. Type 2 diabetes risk, which has a good predictive value up to four years from its onset New tools for assessment are offered.
ALTYAPI Diyabetin mutlak küresel ekonomik yükünün 2015 yilinda 1.3 trilyon ABD dolarindan yükselecegi tahmin edilmektedir, bu da küresel GSYIH'ninm %2.2'sini olusturur. Yalnizca ABD'de, diyabet teshisi konmus bir hastanin ortalama tibbi harcamasi 16.752 dolardir, bu da diyabet olmadiginda yapilacak harcamalardan yaklasik 2.3 kat daha fazladir. Bazi saglik bakim sistemlerinde diyabet hastalarinin bakimi tüm masraflarin %25'ini olusturur. INFRASTRUCTURE The absolute global economic burden of diabetes was more than US$1.3 trillion in 2015. It is expected that this will increase It also accounts for 2.2% of global GDP. In the USA only, a person diagnosed with diabetes the patient's average medical expense is $16,752, which would be done in the absence of diabetes It is about 2.3 times more than expenditures. Diabetes in some healthcare systems Caring for their patients accounts for 25% of all costs.
Hastalik riskinin yeterince erken tespit edilebilmesi halinde, yasam tarzi müdahalesi yoluyla tip 2 diyabetin (T2D) önlenmesi mümkündürlzl. Günümüzde, diyabet risklerini degerlendirmek üzere oral glikoz tolerans testi (OGTT) ve açlik plazma glikozu (FPG) kullanilir. Bununla birlikte, anormal glikoz seviyeleri tespit edilene kadar diyabet ve hatta komplikasyonlar meydana gelmis olabilir. Ayrica, diyabet sistemik bir hastaliktir ve birden fazla kan imzasinin degismesine neden olabilir, bu da diyabet riskini yalnizca glikoza dayali olarak degerlendirmeyi sorgulanabilir hale getirir. Bununla birlikte, mevcut klinik uygulamada, potansiyel bir diyabet teshisi yalnizca glikoz ölçümleri ile degerlendirilir: bir hasta, yüksek kan glikoz seviyelerine (diyabetik) veya normal glikoz seviyelerine (diyabetik olmayan) sahiptir. Bununla birlikte, "diyabetik olmayanlar" arasinda, her bir birey, günümüzde mevcut teknikler ve biyolojik markör ölçümleri ile verimli bir sekilde degerlendirilemeyecek olan, gelecekte diyabet gelistirme risk seviyelerine sahip olabilir. Bu nedenle tip 2 diyabet riskinin çok degiskenli bireysellestirilmis risk puanlari ile degerlendirilmesi uygun bulunmustur. Lifestyle intervention if disease risk can be detected early enough It is possible to prevent type 2 diabetes (T2D) through Today, diabetes risks oral glucose tolerance test (OGTT) and fasting plasma glucose (FPG) to assess used. However, until abnormal glucose levels are detected, diabetes and even complications may have occurred. In addition, diabetes is a systemic disease and can cause multiple blood signatures to change, which only increases the risk of diabetes. makes the assessment based on glucose questionable. However, available In clinical practice, a potential diagnosis of diabetes can only be made with glucose measurements. assessed: a patient has high blood glucose levels (diabetic) or normal glucose levels (non-diabetic). However, "non-diabetic" between each individual, with currently available techniques and biological marker measurements. risk of developing diabetes in the future, which cannot be efficiently assessed may have levels. Therefore, the risk of type 2 diabetes is very variable. It was found appropriate to evaluate it with individualized risk scores.
Mevcut raporda ve çalismada, bulus sahibi, diyabeti olmayan bireyleri farkli biyolojik markörler içeren farkli risk gruplarina kümelemeye yönelik araçlarin gelisimini rapor eder. Ayrica bulus sahibi, diyabet risklerini dogru bir sekilde tahmin edebilecek bir matematiksel model olusturmustur. In the present report and study, the inventor includes individuals without diabetes in different biological Report the development of tools for clustering into different risk groups containing markers it does. In addition, the inventor has a tool that can accurately predict diabetes risks. created a mathematical model.
Sasirtici bir sekilde, follistatinin, burada kullanildigi bildirilen tip 2 diyabetin erken teshisine yönelik bir biyomarkör olarak kullanilabilecegi bulunmustur. Ayrica, bir insanda tip 2 diyabetin erken tahminine yönelik bir biyomarkör imzasi olusturmaya yönelik bir yöntem burada açiklanir. Surprisingly, follistatin is reported to be used here in the early stages of type 2 diabetes. It has been found that it can be used as a biomarker for diagnosis. Also, in a human a biomarker signature for early prediction of type 2 diabetes. The method is described here.
SEKILLERIN KISA AÇIKLAMASI Sekil 1: Karaciger hücresi follistatin salgisi, GCKR-GCK kompleksi tarafindan kontrol Sekil 2: Diyabet ilerleme kohort kümelemesi. BRIEF DESCRIPTION OF THE FIGURES Figure 1: Liver cell follistatin secretion controlled by the GCKR-GCK complex Figure 2: Diabetes progression cohort clustering.
Sekil 3: Bes degiskenin (plazma follistatin, proinsülin, insülin, C-peptid, baslangiç Hbch) önemi ve degiskenlerin seçimi (Sekil 3A, 3B, BC). Figure 3: Five variables (plasma follistatin, proinsulin, insulin, C-peptide, baseline Hbch) significance and selection of variables (Figure 3A, 3B, BC).
Sekil 4: Kohortta 4 yillik tip 2 diyabet insidansi riskini degerlendirmek üzere dört modelin performansi ve geçerliligi. Figure 4: Four models to assess the 4-year risk of type 2 diabetes incidence in the cohort performance and validity.
Sekil 5: 10 kat çapraz geçerlilige sahip dört biyomarkör ile seçilen modelin (NNET) ROC Tablo 1: Farkli yöntemler ile farkli degiskenler ile 10 kat çapraz geçerliligin ROC AUC'si. Figure 5: ROC of the selected model (NNET) with four biomarkers with 10-fold cross validation Table 1: ROC AUC of 10-fold cross-validation with different variables with different methods.
Tablo 2: 10 kat çapraz geçerlilik ile her kümenin performansi. Table 2: Performance of each cluster with 10-fold cross-validation.
Tablo 3: Her sinifa yönelik follistatin içeren ve içermeyen modeller arasindaki AUC'lerin karsilastirmasi. Table 3: AUCs between models with and without follistatin for each class comparison.
DETAYLI AÇIKLAMA Mevcut bulus, bulus sahibi tarafindan, follistatinin bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimine yönelik bir biyomarkör oldugu yönündeki sasirtici kavrayisina dayanir. DETAILED DESCRIPTION The present invention has been proposed by the inventor that follistatin is a short-term, short-term treatment of type 2 diabetes in a human. to his surprising notion that it is a biomarker for high-risk development. it lasts.
Buna göre mevcut bulus, durumunun tedavi edilmemesi halinde, 10 yildan daha kisa, örnegin 9 yildan daha kisa, 8 yildan daha kisa, 7 yildan daha kisa, 8 yildan daha kisa, 5 yildan daha kisa veya 4 yildan daha kisa bir süre içinde tip 2 diyabet gelistirme açisindan yüksek risk tasiyan bir bireyin teshisi ve/veya tahmini ile ilgilidir. Erken teshis ve/veya tahminin özel bir avantaji, önleyici tedavi ile hastalik olusumunu önleme yetenegidir. Accordingly, the present invention, if the condition is not treated, is less than 10 years, for example, less than 9 years, less than 8 years, less than 7 years, less than 8 years, 5 in terms of developing type 2 diabetes in less than a year or less than 4 years relates to the diagnosis and/or prediction of an individual at high risk. Early diagnosis and/or A particular advantage of prediction is its ability to prevent disease occurrence with preventive treatment.
Dolayisiyla bulusun bir birinci düzenlemesinde ve açisinda, bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin teshis edilmesine yönelik bir yöntemde bir biyomarkör olarak follistatin kullanimi detayli olarak anlatilir. Thus, in a first embodiment and aspect of the invention, in a human in a method for diagnosing the development of high-risk type 2 diabetes The use of follistatin as a biomarker is explained in detail.
Bulusun birinci düzenlemesinin ve açisinin baska bir açisinda, bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin tahmin edilmesine yönelik bir yöntemde bir biyomarkör olarak follistatin kullanimi detayli olarak anlatilir. In another aspect of the first embodiment and aspect of the invention, in a human in a method for predicting the development of high-risk type 2 diabetes. The use of follistatin as a biomarker is explained in detail.
Birinci açinin bir düzenlemesinde, birinci düzenlemeye göre follistatin kullanimi detayli olarak anlatilir, burada bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini teshis etme ve/veya tahmin etme yöntemi, 10 yildan daha kisa, tercihen 9 yildan daha kisa, 8 yildan daha kisa, 7 yildan daha kisa, 6 yildan daha kisa, 5 yildan daha kisa veya 4 yildan daha kisa bir süre içinde tip 2 diyabetin yüksek riskli gelisimidir. In one embodiment of the first aspect, the use of follistatin according to the first embodiment is detailed. described here as a short-term, high-risk development of type 2 diabetes in a human. method of diagnosing and/or estimating less than 10 years, preferably more than 9 years short, less than 8 years, less than 7 years, less than 6 years, less than 5 years, or High-risk development of type 2 diabetes in less than 4 years.
Birinci açinin bir düzenlemesinde, önceki herhangi bir düzenlemeye göre follistatin kullanimi detayli olarak anlatilir, burada bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini teshis etme ve/veya tahmin etme yöntemi, baslangiç HbA1C, proinsülin, C-peptid veya 48 aylik Hbch'den seçilen en az bir baska biyomarkör, tercihen baslangiç HbAqC` proinsülin, C-peptid veya 48 aylik HbAqgden seçilen en az iki, en az 3 veya en az 4 baska biyomarkör kullanarak tip 2 diyabet ilerleme risk seviyelerini degerlendirmek üzere k-araçlari kümelemeyi içerir. In one embodiment of the first aspect, follistatin in any previous embodiment Its use is described in detail, where short-term, high- method of diagnosing and/or predicting risk development, initial HbA1C, proinsulin, At least one other biomarker selected from C-peptide or 48-month-old Hbch, preferably initial At least two, at least 3 or at least selected from HbAqC` proinsulin, C-peptide or 48 months old HbAqg Assessing risk levels of progression to type 2 diabetes using 4 other biomarkers Includes k-means clustering.
Birinci açinin bir düzenlemesinde, önceki herhangi bir düzenlemeye göre follistatin kullanimi detayli olarak anlatilir, burada bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini teshis etme ve/veya tahmin etme yöntemi, bir risk tahmin modeli olusturmaya yönelik mevcut biyomarkörlerin özyineleme özelligin ortadan kaldirilmasi ile degerlendirilmesini içerir. In one embodiment of the first aspect, follistatin in any previous embodiment Its use is described in detail, where short-term, high- method of diagnosing and/or predicting risk development, a risk prediction model By eliminating the recursion feature of existing biomarkers for generating includes evaluation.
Birinci açinin bir düzenlemesinde, önceki herhangi bir düzenlemeye göre follistatin kullanimi detayli olarak anlatilir, burada bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini teshis etme ve/veya tahmin etme yöntemi, follistatinden ve baslangiç Hbch, proinsülin, C-peptid veya 48 aylik HbA1c'den seçilen en az bir baska biyomarkörden elde edilen kan seviyelerinin ölçülmesini ve 10 yildan daha kisa bir süre içinde, tercihen 5 yildan daha kisa bir süre içinde veya daha tercihen 4 yildan daha kisa bir süre içinde tip 2 diyabet gelistirme riski yüksek olan bir grup insandan alinan ortalama kan seviyesi degerlerine dayali bir model degeri ile ölçülen kan seviyelerinin karsilastirilmasini içerir. In one embodiment of the first aspect, follistatin in any previous embodiment Its use is described in detail, where short-term, high- method of diagnosing and/or predicting risk development from follistatin and initial At least one other selected from Hbch, proinsulin, C-peptide or 48-month HbA1c measurement of blood levels from the biomarker and for less than 10 years preferably less than 5 years or more preferably less than 4 years average from a group of people at high risk of developing type 2 diabetes over a period of time blood levels as measured by a model value based on blood level values includes comparison.
Birinci açinin bir düzenlemesinde, önceki herhangi bir düzenlemeye göre follistatin kullanimi detayli olarak anlatilir, burada bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini teshis etme ve/veya tahmin etme yöntemi, baslangiç HbAic, proinsülin, C-peptid veya 48 aylik HbA1c'den seçilen en az iki, en az 3 veya en az 4 baska biyomarkörden olusan kari seviyelerinin ölçülmesini içerir. In one embodiment of the first aspect, follistatin in any previous embodiment Its use is described in detail, where short-term, high- method of diagnosing and/or predicting risk development, initial HbAic, proinsulin, At least two, at least 3 or at least 4 other selected from C-peptide or 48 months old HbA1c includes measuring the levels of snow formed from the biomarker.
Birinci açinin bir düzenlemesinde, önceki herhangi bir düzenlemeye göre follistatin kullanimi detayli olarak anlatilir, burada bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini teshis etme ve/veya tahmin etme yöntemi, bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini tahmin etme yöntemidir. In one embodiment of the first aspect, follistatin in any previous embodiment Its use is described in detail, where short-term, high- method of diagnosing and/or predicting the risky development of type 2 diabetes in a human It is a method of predicting long-term, high-risk development.
Bulusun ikinci bir açisinda, follistatinden ve baslangiç HbA1c, proinsülin, C-peptid veya 48 aylik HbA15den seçilen en az bir baska biyomarkörden elde edilen kan seviyelerinin ölçülmesini ve 10 yildan daha kisa bir süre içinde, tercihen 5 yildan daha kisa bir süre içinde veya daha tercihen 4 yildan daha kisa bir süre içinde tip 2 diyabet gelistirme riski yüksek olan bir grup insandan alinan ortalama kan seviyesi degerlerine dayali bir model degeri ile ölçülen kan seviyelerinin karsilastirilmasini içeren bir insanda tip 2 diyabetin erken tahminine yönelik detayli bir biyomarkör imzasi olusturma yöntemi vardir. In a second aspect of the invention, follistatin and initial HbA1c, proinsulin, C-peptide or Blood levels from at least one other biomarker selected from 48 months of HbA15 be measured and in less than 10 years, preferably less than 5 years risk of developing type 2 diabetes within a year, or more preferably in less than 4 years a model based on average blood levels from a group of people with high of type 2 diabetes in a human, which involves comparison of blood levels measured with There is a detailed biomarker signature generation method for its early prediction.
Ikinci açinin bir düzenlemesinde, baslangiç HbA1c, proinsülin, C-peptid veya 48 aylik HbA1c'den seçilen en az iki, en az 3 veya en az 4 baska biyomarkörün kan seviyeleri Benzer sekilde, bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisiminin teshisinde ve/veya tahmininde bir biyomarkör olarak kullanima yönelik burada detayli follistatin Bunun bir düzenlemesinde, bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin teshisinde ve/veya tahmininde bir biyomarkör olarak kullanima yönelik detayli follistatin vardir, burada insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimi, 10 yildan daha kisa, tercihen 9 yildan daha kisa, 8 yildan daha kisa, 7 yildan daha kisa, 6 yildan daha kisa, 5 yildan daha kisa veya 4 yildan daha kisa bir süre içinde tip 2 diyabetin yüksek riskli gelisimidir. In one embodiment of the second aspect, baseline HbA1c, proinsulin, C-peptide, or 48 months Blood levels of at least two, at least 3, or at least 4 other biomarkers selected from HbA1c Similarly, in the diagnosis of short-term, high-risk development of type 2 diabetes in a human and/or follistatin for use as a predictive biomarker detailed here In one embodiment of this, short-term, high-risk type 2 diabetes development in a human Detailed follistatin for use as a biomarker in the diagnosis and/or prediction of where there is a short-term, high-risk development of type 2 diabetes in humans, more than 10 years short, preferably less than 9 years, less than 8 years, less than 7 years, more than 6 years short, high incidence of type 2 diabetes in less than 5 years or less than 4 years risky development.
Bunun bir düzenlemesinde, bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin teshisinde ve/veya tahmininde bir biyomarkör olarak kullanima yönelik detayli follistatin vardir, burada bir insanda tip 2 diyabetin kisa süreli, yüksek riskli gelisimini teshis etme, baslangiç HbAic, proinsülin, C-peptid veya 48 aylik HbAic'den seçilen en az bir baska biyomarkör, tercihen baslangiç HbAic, proinsülin, C-peptid veya 48 aylik HbA1c'den seçilen en az iki, en az 3 veya en az 4 baska biyomarkör kullanarak tip 2 diyabet ilerleme risk seviyelerini degerlendirmek üzere k-ortalama kümelemeyi içerir. In one embodiment of this, short-term, high-risk type 2 diabetes development in a human Detailed follistatin for use as a biomarker in the diagnosis and/or prediction of where to diagnose a short-term, high-risk development of type 2 diabetes in a person, at least one other selected from baseline HbAic, proinsulin, C-peptide, or 48-month HbAic biomarker, preferably from baseline HbAic, proinsulin, C-peptide or 48-month HbA1c type 2 diabetes progression using at least two, at least 3, or at least 4 other selected biomarkers includes k-means clustering to assess risk levels.
Bunun bir düzenlemesinde, bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin teshisinde ve/veya tahmininde bir biyomarkör olarak kullanima yönelik detayli follistatin vardir, burada bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin teshisi, bir risk tahmin modelinde özyinelemeli özelligin ortadan kaldirilimasi yoluyla mevcut biyomarkörlerin degerlendirilmesini içerir. In one embodiment of this, short-term, high-risk type 2 diabetes development in a human Detailed follistatin for use as a biomarker in the diagnosis and/or prediction of where the diagnosis of short-term, high-risk type 2 diabetes development in a person is existing through the elimination of the recursive feature in the risk prediction model. Includes evaluation of biomarkers.
Bunun bir düzenlemesinde, follistatinden ve baslangiç HbAic, proinsülin, C-peptid veya 48 aylik HbAiJden seçilen en az bir baska biyomarkörden elde edilen kan seviyelerinin ölçülmesini ve 10 yildan daha kisa bir süre içinde, tercihen 5 yildan daha kisa bir süre içinde veya daha tercihen 4 yildan daha kisa bir süre içinde tip 2 diyabet gelistirme riski yüksek olan bir grup insandan alinan ortalama kan seviyesi degerlerine dayali bir model degeri ile ölçülen kan seviyelerinin karsilastirilmasini içeren, bir insanda tip 2 diyabetin erken tahminine yönelik bir biyomarkör imzasi olusturmayi içeren, bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin teshisinde ve/veya tahmininde bir biyomarkör olarak kullanima yönelik detayli follistatin vardir. In one embodiment of this, follistatin and initial HbAic, proinsulin, C-peptide or Blood levels from at least one other biomarker selected from 48 months of HbAiJ be measured and in less than 10 years, preferably less than 5 years risk of developing type 2 diabetes within a year, or more preferably in less than 4 years a model based on average blood levels from a group of people with high of type 2 diabetes in a human, which involves comparison of blood levels measured with short-term in a human, involving generating a biomarker signature for early prediction as a biomarker in the diagnosis and/or prediction of the development of high-risk type 2 diabetes. There is detailed follistatin for use.
Bunun bir düzenlemesinde, ayrica baslangiç HbA1c, proinsülin, C-peptid veya 48 aylik HbAic'den seçilen en az iki, en az 3 veya en az 4 baska biyomarkörden olusan kan seviyelerinin ölçülmesini içeren, bir insanda kisa süreli, yüksek riskli tip 2 diyabet gelisiminin teshisinde ve/veya tahmininde bir biyomarkör olarak kullanima yönelik detayli follistatin vardir. In one embodiment of this, additional baseline HbA1c, proinsulin, C-peptide or 48-month-old Blood consisting of at least two, at least 3, or at least 4 other biomarkers selected from HbAic short-term, high-risk type 2 diabetes in a person, which involves measuring the levels of detailed information on its use as a biomarker in the diagnosis and/or prediction of its development. It has follistatin.
Mevcut bulus, durumunun tedavi edilmemesi halinde, 10 yildan daha kisa, örnegin 9 daha kisa veya 4 yildan daha kisa bir süre içinde tip 2 diyabet gelistirme açisindan yüksek risk tasiyan bir bireyin teshisi ve tanimlanmasi ile ilgilidir. Erken teshisin özel bir avantaji, önleyici tedavi ile hastalik olusumunu önleme yetenegidir. The present invention is available for less than 10 years if the condition is not treated, for example 9 for developing type 2 diabetes in less time or less than 4 years It is concerned with diagnosing and identifying an individual at high risk. A special feature of early diagnosis The advantage is the ability to prevent disease occurrence with preventive treatment.
Bulusa göre mevcut bulus, durumunun tedavi edilmemesi halinde, 10 yildan daha kisa, örnegin 9 yildan daha kisa, 8 yildan daha kisa, 7 yildan daha kisa, 6 yildan daha kisa, 5 yildan daha kisa veya 4 yildan daha kisa bir süre içinde bir insanda tip 2 diyabet gelistirme riskinin teshis edilmesi ve/veya tahmin edilmesi ile ilgilidir. According to the present invention, if the condition is not treated, less than 10 years, For example, less than 9 years, less than 8 years, less than 7 years, less than 6 years, 5 type 2 diabetes in a person in less than a year or less than 4 years relates to diagnosing and/or estimating development risk.
Bir düzenlemede, bir birey kan serumunda en az 2000 pg/mL follistatin sundugunda, kisa bir süre içinde tip 2 diyabet gelistirme riski mevcuttur. In one embodiment, when an individual presents at least 2000 pg/mL of follistatin in their blood serum, There is a risk of developing type 2 diabetes in a short period of time.
Bunun bir düzenlemesinde, ayrica bir birey kan serumunda en az 20 pmol/L proinsülin sundugunda, kisa bir süre içinde tip 2 diyabet gelistirme riski mevcuttur. In one embodiment of this, an individual may also have at least 20 pmol/L proinsulin in their blood serum. present, there is a risk of developing type 2 diabetes in a short period of time.
Bunun bir düzenlemesinde, ayrica bir birey kan serumunda en az 5 ng/mL C-peptid sundugunda, kisa bir süre içinde tip 2 diyabet gelistirme riski mevcuttur. In one embodiment of this, also an individual blood serum of at least 5 ng/mL of C-peptide present, there is a risk of developing type 2 diabetes in a short period of time.
Bunun bir düzenlemesinde, ayrica bir birey kari serumunda en az 800 pgi'mL insülin sundugunda, kisa bir süre içinde tip 2 diyabet gelistirme riski mevcuttur. In one embodiment of this, there is also an individual serum of at least 800 pgi'mL of insulin. present, there is a risk of developing type 2 diabetes in a short period of time.
Asagidaki örneklerde belgelendigi gibi, yukarida ve örneklerde verilen kan serumu follistatin veya follistatin seviyeleri ve yukaridaki biyolojik markörlerden bir veya daha fazlasi ile açlik gösteren bireylerin, ilerlemeyen prediyabetik ve diyabetik olmayan bireylerden olusan bir kontrol popülasyonu üzerinde istatistiksel anlamlilik ile HbA1C ve 48 aylik HbA1C kullanilarak ölçüldügü üzere tip 2 diyabet gelistirdigi gözlemlenmistir ve bu nedenle tip 2 diyabet gelistirme riski yüksek olan bir popülasyondur. Kendi baslarina, tollistatin dikkate deger muafiyeti ile birlikte, her bir diger markör, popülasyonlar arasinda istatistiksel olarak ayirt edilemezdir, ancak markörlerin kombinasyonu, net bir tanimlama saglamistir. Follistatine yönelik, 2500 pg/mL'nin üzerindeki, tercihen 3000 pg/mL'nin üzerindeki kan serum seviyeleri, incelenen bireyde tip 2 diyabet gelistirme riskinin yüksek oldugunu göstermede tek basina anlamlidir. ÖRNEKLER Raporlanan çalismanin amaci Bu çalismanin amaci, kan biyomarkör imzasi ile tip 2 diyabet (T2D) riskini degerlendirmek üzere bir tahmin modeli gelistirmektir. As documented in the examples below, the blood serum given above and in the samples follistatin or follistatin levels and one or more of the above biological markers severely starving individuals, non-progressive prediabetic and non-diabetic HbA1C and HbA1C with statistical significance on a control population of individuals It has been observed to develop type 2 diabetes as measured using 48-month HbA1C and therefore, it is a population at high risk of developing type 2 diabetes. On their own, With notable exemption from tollistat, each other marker differed between populations. are statistically indistinguishable, but the combination of markers provides a clear definition. it is solid. Above 2500 pg/mL, preferably 3000 pg/mL for follistatin blood serum levels above It is significant on its own in showing that it exists. EXAMPLES Purpose of reported study The aim of this study is to determine the risk of type 2 diabetes (T2D) by blood biomarker signature. Developing a forecasting model to evaluate
Arastirma tasarimi ve yöntemleri Çalisma bireyleri, T2D ilerlemesine yönelik dört yillik takipli 152 diyabetli olmayan katilimciyi içeren uzunlamasina bir kohorttandir. Kohort, baslangiç HbA1c, proinsülin, C- peptid, follistatin ve 48 aylik HbA'ic kullanilarak T2D ilerleme risk seviyelerini degerlendirmek üzere k-araçlari ile kümelenmistir. Mevcut biyomarkörler, risk tahmin modelini olusturmak üzere özyinelemeli özelligin ortadan kaldirilmasi ile degerlendirilmistir. Risk kümelemeye dayali T2D dört yillik tahmin, Sinir Agi (NNET), Destek Vektör Makinesi (SVM), Rastgele Orman (RF) ve Genellestirilmis Lojistik Regresyon (GLM) makine ögrenme yöntemleri ile test edilmistir. Dört aday risk modelinin performansi, 10 kat çapraz geçerlilik kullanilarak degerlendirilmistir. Research design and methods Study subjects included 152 nondiabetic subjects with four-year follow-up for T2D progression. from a longitudinal cohort that included the participant. Cohort, baseline HbA1c, proinsulin, C- T2D progression risk levels using peptide, follistatin and 48-month HbA'ic clustered with k-means for evaluation. Current biomarkers, risk estimation with the elimination of the recursive feature to create the model has been evaluated. T2D four-year forecast based on risk clustering, Neural Network (NNET), Support Vector Machine (SVM), Random Forest (RF), and Generalized Logistics It has been tested with regression (GLM) machine learning methods. Four candidate risk models performance was evaluated using 10-fold cross-validation.
Genel sonuçlar Kohort üç risk grubuna kümelenmistir: yüksek risk, orta risk ve düsük risk. Baslangiç Hbch, proinsülin, C-peptid ve follistatin, biyomarkör geçerliliginden sonra seçilmistir. Bir bireyin 4 yillik T2D gelistirme riskini degerlendirmeye yönelik optimal bir model, bu dört biyomarkör kullanilarak NNET makine ögrenimi yöntemi ile gelistirilmistir. Her bir risk grubunun tahminine yönelik alici isletim karakteristigi (ROC) egrisinin egri altinda kalan çapraz geçerlilik). Üç risk grubunun ortalama AUC'u, 0.97'dir. overall results The cohort is clustered into three risk groups: high risk, intermediate risk, and low risk. Beginning Hbch, proinsulin, C-peptide and follistatin were selected after biomarker validation. One An optimal model for assessing an individual's 4-year risk of developing T2D, these four It was developed by NNET machine learning method using biomarker. each risk under the curve of the receiver operating characteristic (ROC) curve for the estimation of cross validation). The average AUC of the three risk groups was 0.97.
Buna göre, burada, HbA'lci proinsülin, C-peptid ve follistatin dahil olmak üzere dört kan biyomarkör içeren bir model ile hastalik baslangicindan dört yil önce tip 2 diyabet risk degerlendirmesine yönelik yeni araçlar sunulur. Accordingly, here are four blood cells, including proinsulin with HbA, C-peptide and follistatin. type 2 diabetes risk four years before disease onset with a biomarker-containing model New tools for assessment are offered.
YÖNTEM LER Kohort katilimcilari Kohort, ABD'de yapilan bir klinik arastirmanin çok merkezli, randomize, çift kör, plasebo kontrolüdür. HbA1C ölçülmüstür ve hastalarda baslangiçta, 1. yilda, 2. yilda ve 4. yilda birlikte kullanilan ilaç maddeleri belgelenmistir. Yaklasik 400 hastadan olusan bir T2D ilerleme alt kohortu, 4 yillik deneme süresi boyunca baslangiçtan hasta HbAiC degisikligine ve bu hastalara diyabet ilaci uygulanmamasina dayali olarak kohorttan seçilmistir. METHODS Cohort participants The cohort is a multicenter, randomized, double-blind, placebo-based clinical trial conducted in the USA. is control. HbA1C was measured and patients were evaluated at baseline, year 1, year 2, and year 4. Concomitant use of drugs has been documented. A T2D population of approximately 400 patients progression sub-cohort, sick at baseline during 4-year trial HbAiC from the cohort based on the change in diabetes mellitus and the lack of diabetes medication in these patients. has been selected.
PLAZMA PROTEIN BIYOBELIRTEÇ ÖLÇÜMLERI Açlik insülini, Pro-insülin, C-peptid ve Follistatin, tip 2 diyabet ilerleme kohortuna dahil edilmek üzere seçilen 314 hastadan elde edilen baslangiç EDTA-plazma numunelerinde enzime bagli immünosorbent tahlili (ELISA) ile ölçülmüstür. C-peptid disindaki tüm tahlillerde, numuneler, teknik kopyalarda ölçülmüstür ve müteakip tahlil içi ortalama varyasyon katsayisi (%CV) hesaplanmistir. Tahliller arasi %CV, iç kontroller kullanilarak hesaplanmistir. Insülin konsantrasyonlari, özel olarak olusturulmus bir elektro kemilüminesans immünolojik tahlili ve dahili olarak optimize edilmis bir protokoll3'51 izlenerek bir MESO QuickPlex SQ , Gaithersburg, MD) kullanilarak belirlenmistir. Insülin tahlillerine yönelik tahlil içi CV, %10.9'dur ve tahliller arasi CV, %3.1 olmustur. C-peptit seviyeleri, Cobas e411 (Roche Diagnostics, Mannheim, Almanya) kullanilarak bir elektro kemilüminesans immünolojik tahlili ile ölçülmüstür. Intakt Pro-insülin, üreticinin talimatlari izlenerek (IRP 84/811; katalog # IV2- 102E, Immuno-Biological Laboratories, Inc., Minneapolis, MN) WHO 1. Uluslararasi Pro- insülin Standardina göre kalibratörler ile kolorimetrik bir ELISA kullanilarak numune tamponunda 4 kat seyreltmeden sonra ölçülmüstür. Tahlil absorbansi, bir PHERAstar FSX (BMG Labtech Inc., Cary, NC) kullanilarak ölçülmüstür. Pro-insülin tahlillerine yönelik tahlil içi CV %66 ve tahliller arasi CV %10 olmustur. Plazma Follistatin seviyeleri, üreticinin talimatlarina göre (katalog # DFNOO, R&D Systems, Minneapolis, MN) kolorimetrik bir ELISA kullanilarak numune seyrelticide 2 kat seyreltmeden sonra ölçülmüstür. Tahlil absorbansi, bir PHERAstar FSX (BMG Labtech Inc., Cary, NC) kullanilarak ölçülmüstür. Follistatin tahlillerine yönelik tahlil içi CV, %2.1'dir ve tahliller arasi CV, %10 olmustur. PLASMA PROTEIN BIOBELETE MEASUREMENTS Fasting insulin, Pro-insulin, C-peptide and Follistatin included in type 2 diabetes progression cohort in baseline EDTA-plasma samples from 314 patients selected for measured by enzyme-linked immunosorbent assay (ELISA). All except the C-peptide In assays, samples were measured in technical replicates and the subsequent within-assay average coefficient of variation (%CV) was calculated. CV% between assays, using internal controls calculated. Insulin concentrations are determined by a specially created electro chemiluminescence immunoassay and an internally optimized protocol3'51 following a MESO QuickPlex SQ, Gaithersburg, MD) was determined using The in-assay CV for insulin assays is 10.9% and Inter-assay CV was 3.1%. C-peptide levels, Cobas e411 (Roche Diagnostics, Mannheim, Germany) by an electrochemiluminescence immunoassay using has been measured. Intakt Pro-insulin, following the manufacturer's instructions (IRP 84/811; catalog # IV2- 102E, Immuno-Biological Laboratories, Inc., Minneapolis, MN) WHO 1st International Pro- Sample using a colorimetric ELISA with calibrators to the Insulin Standard Measured after 4 fold dilution in buffer. Assay absorbance, a PHERAstar Measured using FSX (BMG Labtech Inc., Cary, NC). Pro-insulin assays The intra-assay CV for the assay was 66% and the inter-assay CV was 10%. Plasma Follistatin levels, according to manufacturer's instructions (catalog # DNFOO, R&D Systems, Minneapolis, MN) After 2-fold dilution in sample diluent using a colorimetric ELISA has been measured. Assay absorbance, a PHERAstar FSX (BMG Labtech Inc., Cary, NC) measured using. The in-assay CV for follistatin assays is 2.1% and assays Intermediate CV was 10%.
Model gelistirme prosesi: 4 yillik tip 2 diyabet riskini tasarlamak üzere makine ögrenimini kullanma Tüm istatistiksel analizler, istatistiksel analiz yazilim paketi, makine ögrenimi araç seti (Caret paketilel) ve istatistiksel hesaplama ortami Rm kullanilarak gerçeklestirilmistir. Model development process: machine to design 4-year risk of type 2 diabetes using your learning All statistical analysis, statistical analysis software package, machine learning toolkit (Caret package) and statistical computation environment was performed using Rm.
Sonuçlara yönelik anlamlilik ps0.05 olarak belirlenmistir. Özellik seçimi, en iyi tahmin performansina sahip bu kan parametrelerini tanimlamak üzere makine ögrenimi R paketi Caret'te (örnegin rfe, rfefilter) uygulanan özyinelemeli özelligi ortadan kaldirma yöntemi ile gerçeklestirilmistir. Bes aday biyomarkör, çoklu markör modellere dahil edilmek üzere degerlendirilmistir. Tahmine dayali teknikler veriler üzerinde farkli performans gösterebildiginden, R ortamindam Caret paketinilö] kullanarak dört farkli tahmine dayali model kullanmayi seçiyoruz. NNET (Sinir agi), SVM (Destek vektör makineleri), RF (Rastgele orman yöntemleri) ve GLM (Genellestirilmis lojistik regresyon) dahil olmak üzere dört makine yaklasimi dahil edilmistir. Kan bazli bir biyomarkör panelinin tip 2 diyabet gelistirme riskinin tahminine izin verip vermedigini arastirmak üzere çok degiskenli veri analizi (NNET, SVM, RF ve GLM) kullanilmistir. En iyi performans gösteren NNET modeli, sonuçlarin saglamligini saglamak üzere 10 kat çapraz geçerlilik prosedüründe degerlendirilmistir. Asagidaki karakteristik sayilar hesaplanmistir: AUC, dogruluk, duyarlilik ve özgüllük. The significance for the results was determined as ps0.05. Feature selection is to identify those blood parameters with the best predictive performance. recursive, implemented in the machine learning R package Caret (e.g. rfe, rfefilter) carried out by the feature elimination method. Five candidate biomarkers, multiple The marker was evaluated for inclusion in the models. predictive techniques data Using the Caret package in the R environment] We choose to use four different predictive models. NNET (Neural network), SVM (Support vector machines), RF (Random forest methods), and GLM (Generalized logistics) Four machine approaches are included, including regression. a blood-based whether the biomarker panel allows estimation of the risk of developing type 2 diabetes Multivariate data analysis (NNET, SVM, RF and GLM) was used to investigate. Most The well-performing NNET model is 10x to ensure the robustness of the results. evaluated in the cross-validation procedure. The following characteristic numbers were calculated: AUC, accuracy, sensitivity, and specificity.
Son olarak alici isletim karakteristigi (ROC) egrileri olusturulmustur. DeLong testi, R kitapligi pROC'ninl81 iki korelasyonlu ROC egrisine (diger bir deyisle, Follistatin içermeyen NNET ve NNET tarafindan yüksek riskli ROC egrileri) yönelik roc testi kullanilarak gerçeklestirilmistir. P degerleri <0.05, anlamli olarak kabul edilmistir. Finally, receiver operating characteristic (ROC) curves were created. DeLong test, R library of pROC'sl81 has two correlated ROC curves (i.e., Follistatin roc testing for high-risk ROC curves by NNET and NNET without carried out using P values <0.05 were considered significant.
Sonuçlar, kisa süreli, yüksek riskli tahminin sonucunun modelden bagimsiz oldugunu, dolayisiyla çalisilan gruplarin istatistiklerinin altinda yatan gerçek biyolojik prosesleri yansittigini göstermistir. The results show that the outcome of the short-term, high-risk prediction is model independent, hence the actual biological processes underlying the statistics of the studied groups. has shown that it reflects.
SONUÇLAR Plazma follistatin seviyelerini etkileyen genetik faktörleri tanimlamak üzere iki farkli kohortta GWAS gerçeklestirdik. Glukokinaz düzenleyici protein (GCKR), plazma follistatin seviyelerinin genetik düzenleyicisi olarak tanimlanmistir. Burada GCKR'nin, bir insan hepatosit hücre dizisi HepG2'de glukagon ve insülin ile birlikte karaciger follistatin salgisini düzenledigini gösteriyoruz. Önceki arastirmalar, GCKR'nin çekirdekte GCK ile siki bir kompleks olusturdugunu ve GCK-GCKR baglanmasinin ayrismasinin, karaciger hücresi glikoz alimini ve glikolizini düzenleyen çekirdekten sitoplazmaya artan GCK translokasyonuna yol açtigini göstermistir. RESULTS Two different studies were conducted to identify genetic factors affecting plasma follistatin levels. we performed GWAS in the cohort. Glucokinase regulatory protein (GCKR), plasma It has been defined as a genetic regulator of follistatin levels. Here GCKR has a Liver follistatin with glucagon and insulin in the human hepatocyte cell line HepG2 We show that it regulates its secretion. Previous research has shown that GCKR is compatible with GCK in the kernel. that it forms a tight complex and that the cleavage of the GCK-GCKR binding Increased GCK from nucleus to cytoplasm regulating cell glucose uptake and glycolysis showed that it causes translocation.
Bu çalismada, HepGZ hücreleri, GCK ile transfekte edilmistir veya GCK ve GCKR eksprese eden plazmitler (1z3 molar oran) ile birlikte transfekte edilmistir. Ek olarak, hücreler, ayrismis GCK'nin çekirdekten sitoplazmaya güçlü bir sekilde translokasyonunu destekleyen bir GCK-GCKR kompleksi bozucu molekülü olan AMG-3669 ile ve bunlar olmadan tedavi edilmistir. Hücreler, glukagon (1 pg/ml) ve hücre içi cAMP aktivatörü forskolin (20 uM) ile düsük glikozlu DMEM ortaminda ( inkübe edilmistir, kosullarin daha önce karaciger hücrelerinden” follistatin salgilanmasini uyardigi gösterilmistir. GCK-GCKR kompleksi ve bunun bozucusu AMG-3969'un varliginda, follistatin salgilanmasi, insülin ile birlikte inkübasyon ile tersine çevrilen kontrole (Sekil 4A) kiyasla %40 artmistir (Sekil 48). Tek basina GCK ile transfeksiyon veya GCKR'nin çekirdekten sitoplazmaya translokasyonunu etkilemeyen AMG-3969 olmadan GCK- GCKR birlikte transfeksiyonu, follistatin salgilanmasi üzerinde hiçbir etkiye sahip olmamistir (Sekil 1). In this study, HepGZ cells were transfected with GCK or GCK and GCKR. expressing plasmids (1z3 molar ratio). In addition, cells strongly translocation of dissociated GCK from the nucleus to the cytoplasm. with AMG-3669, a GCK-GCKR complex degrading molecule that promotes without treatment. Cells, glucagon (1 pg/ml) and intracellular cAMP activator incubated with forskolin (20 µM) in low glucose DMEM medium (, conditions previously stimulated the release of follistatin from “liver cells”. shown. In the presence of the GCK-GCKR complex and its disruptor, AMG-3969, follistatin secretion to control, which was reversed by co-incubation with insulin (Fig. 4A) increased by 40% compared to (Fig. 48). Transfection with GCK alone or GCKR GCK- without AMG-3969, which does not affect translocation from the nucleus to the cytoplasm GCKR co-transfection has no effect on follistatin secretion. (Figure 1).
Sekil 1. Karaciger hücresi follistatin salgisi, GCKR-GCK kompleksi tarafindan kontrol edilir. A. Insan karaciger karsinomasindan türetilen HepG2 hücreleri, asagidaki belirtilen plazmitler ile transfekte edilmistir: i) kontrol (pClVIV-XL4, açik çubuklar); ii) Transfeksiyondan kirk sekiz saat sonra, hücreler 3 saat boyunca düsük glikozlu ( DMEM'de serum aç birakilmistir ve ortama 30 dakika boyunca bir GCKR-GCK bozucu molekül AMG-3969 ( ve forskolin (20 pM) içeren serumsuz düsük glikozlu ( DMEM içinde inkübe edilmistir ve karsilik gelen oyuklara AMG-3969 ( eklenmistir. 4 saatlik inkübasyondan sonra ortam, ELISA ile follistatin tahliline yönelik toplanmistir. Follistatin seviyeleri, her numune içindeki protein konsantrasyonuna normallestirilmistir. Kosul basina 3 teknik kopya ile iki bagimsiz deney, farkli plazmit preparasyonlari ve hücre geçis sayilari kullanilarak farkli günlerde gerçeklestirilmistir. B. HepG2 hücreleri, panel A'da açiklandigi gibi, ancak insülin varliginda ( tedavi edilmistir. * p<0.05 ve **p<0.01 belirtildigi gibi. Figure 1. Liver cell secretion of follistatin by the GCKR-GCK complex Is controlled. A. HepG2 cells derived from human liver carcinoma, transfected with the indicated plasmids: i) control (pClVIV-XL4, open bars); ii) Forty-eight hours after transfection, cells were treated with low glucose ( Serum was starved in DMEM and a GCKR-GCK disruptor was applied to the medium for 30 minutes. molecule AMG-3969 ( and Serum-free low glucose (incubated in DMEM) containing forskolin (20 pM) and AMG-3969 ( is added to the corresponding cavities. 4 hours After incubation, the medium was collected for follistatin assay by ELISA. follistatin levels were normalized to the protein concentration in each sample. Condition Two independent experiments with 3 technical replicates per, different plasmid preparations and cell It was carried out on different days using the number of passes. B. HepG2 cells, panel Treated as described in A, but in the presence of insulin ( * p<0.05 and **p<0.01 as specified.
Diyabeti olmayan bireyler arasinda tip 2 diyabet risklerini daha iyi karakterize etmek üzere, ABD kohort katilimcilari, follistatin ve daha önce diyabet veya gelecekteki diyabet riskleri ile iliskili oldugu gösterilen diger degiskenler kullanilarak kümelenmistir. HbAm, proinsülin, C-peptid, follistatin ve 48 aylik HbA1c kullanilarak k-araçlari kümeleme, üç risk grubu tanimlamistir: yüksek risk, orta ve düsük risk gruplari (Sekil 2). Küme 1'deki yüksek risk grubu, 48 ay sonra diyabetli olmayandan diyabete ilerlemistir, medyan HbA1C baslangiçta %5.6'dan 48 ayda %6.8'e yükselmistir; küme 2'deki orta risk grubu, ilerlemeyen prediyabetleri temsil eder (medyan HbA1C baslangiç degeri %62 ila HbA1c 48 aylik %63) ve küme 3'teki düsük risk grubu, ilerlemeyen diyabetik olmayan bireyleri içermistir (medyan HbA1c baslangiç degeri %5.4 ile HbA1C 48 aylik %55) (Sekil 2A). Better characterize the risks of type 2 diabetes among individuals without diabetes U.S. cohort participants, follistatin and pre- or future diabetes mellitus Clustered using other variables shown to be associated with risks. HbAm, C-means clustering using proinsulin, C-peptide, follistatin and 48-month HbA1c, three risks defined the group: high risk, medium and low risk groups (Figure 2). high in cluster 1 risk group progressed from nondiabetic to diabetes after 48 months, median HbA1C increased from 5.6% at baseline to 6.8% at 48 months; intermediate risk group in cluster 2, represents non-progressive prediabetes (median HbA1C baseline 62% to HbA1c 48 months 63%) and the low-risk group in cluster 3 were non-progressive nondiabetic individuals. (median HbA1c baseline value of 5.4% vs. HbA1c 55% at 48 months) (Figure 2A).
Küme 1'deki hastalar, diyabet baslangicindan 48 ay önce (Sekil 28) diger kümelere göre baslangiçta önemli ölçüde daha yüksek plazma follistatin seviyelerine ve daha yüksek baslangiç plazma proinsülin (Sekil 20), C-Peptid (Sekil 2D) ve insülin seviyelerine (Sekil 2E) sahiptir. Patients in Cluster 1, 48 months before onset of diabetes (Figure 28) compared to other clusters at baseline to significantly higher plasma follistatin levels and higher to baseline plasma proinsulin (Figure 20), C-Peptide (Figure 2D), and insulin levels (Figure 20). 2E) has.
Sekil 2. Diyabet ilerleme kohort kümelemesi. ABD kohortundan (n=152) bireyler, 48 ayda baslangiç HbAic, plazma Follistatin, Pro-insülin, C-peptid ve HbA1C kullanilarak denetlenmeyen K-araçlari ile kümelenmistir. A. Küme1: diyabet olmayandan diyabete ilerleme (açik çubuklar; medyan HbA1c baslangiç degeri %56 ila HbA1C 48 aylik %68; n=20); küme2: ilerlemeyen prediyabet (gri çubuklar; medyan HbA1c baslangiç degeri Baslangiç Follistatin (pg/mL, B), Pro insülin (pmoI/L, C), C-peptid (ng/mL, D) ve Insülinin **p<0.01, *p<0.05 belirtildigi gibi. Figure 2. Diabetes progression cohort clustering. Individuals from the US cohort (n=152), 48 1 month using HbAic, plasma Follistatin, Pro-insulin, C-peptide and HbA1C clustered with unsupervised K-means. A. Cluster 1: non-diabetic to diabetic progression (open bars; median HbA1c baseline 56% to HbA1C 68% at 48 months; n=20); cluster2: nonprogressive prediabetes (gray bars; median HbA1c baseline Initial Follistatin (pg/mL, B), Pro insulin (pmoI/L, C), C-peptide (ng/mL, D) and Insulin **p<0.01, *p<0.05 as indicated.
Her baslangiç degiskenin (HbA1C, proinsülin, C-peptid, insülin ve follistatin) ve bunlarin kombinasyonlarinin tahmin gücünü geçerli kilmak üzere, Sinir Agi (NNET), Destek Vektör Makinesi (SVM), Rastgele Orman (RF) ve Genellestirilmis Lojistik Regresyon (GLM) makine ögrenme yöntemleri ile Caretlö] rfe fonksiyonunu kullanarak özyinelemeli özelligin ortadan kaldirilmasini gerçeklestirdik (Tablo 1). Her bir risk grubunun dört yillik tip 2 diyabet tahminine yönelik yüksek risk grubuna yönelik proinsülin ve follistatin, orta ve düsük risk grubuna yönelik Hbchve follistatin en yüksek öneme sahiptir (Sekil SB). Üç risk grubuna yönelik genel önem, maksimum islem ile sunulur (Sekil SC). Dört degiskenin kombinasyonu, en yüksek referans dogrulugunu verir (10 kat çapraz geçerlilik, Sekil 3A). Son olarak, aday biyomarkörler olarak dört üst risk faktörü (baslangiç HbAic, follistatin, proinsülin ve C-peptid) seçilmistir. Each baseline variable (HbA1C, proinsulin, C-peptide, insulin and follistatin) and their Neural Network (NNET), Support Vector Machine (SVM), Random Forest (RF), and Generalized Logistic Regression (GLM) machine learning methods using the Caretlö] rfe function recursively we performed the elimination of the feature (Table 1). four years for each risk group. Proinsulin and follistatin for the high-risk group for predicting type 2 diabetes, moderate and Hbch and follistatin for the low risk group have the highest importance (Figure SB). The overall importance for the three risk groups is presented with maximum action (Figure SC). Four combination of variable gives the highest reference accuracy (10 times cross validity, Figure 3A). Finally, the four top risk factors as candidate biomarkers (initial HbAic, follistatin, proinsulin and C-peptide) were selected.
Sekil 3. Bes degiskenin (plazma follistatin, proinsülin, insülin, C-peptid, baslangiç HbA1c) önemi ve degiskenlerin seçimi. Özyineleme özelligin ortadan kaldirilmasi (3A) kullanilarak farkli degisken ile dogruluk, her degiskenin farkli risk seviyelerindeki katkisi (SB) ve her degiskene yönelik üç risk seviyesinin maksimum puani (SC). Figure 3. Five variables (plasma follistatin, proinsulin, insulin, C-peptide, baseline HbA1c) importance and selection of variables. Elimination of the recursion feature (3A) accuracy with different variables, the contribution of each variable at different risk levels (SB) and the maximum score (SC) of the three risk levels for each variable.
Ardindan, seçilen biyomarkörleri içeren dört yillik tip 2 diyabet risklerinin tahmin performansini çalismak üzere farkli makine ögrenme yöntemleri karsilastirilmistir. Next, the estimation of four-year type 2 diabetes risks involving selected biomarkers Different machine learning methods are compared to study the performance.
NNET, SVM, RF ve GLM makine ögrenme yöntemleri, 10 kat çapraz geçerlilik ile degerlendirilmistir (Tablo 1). Duyarlilik, özgüllük, dogruluk ve AUC (Sekil 4A) araliklari ve güven araliklari (Sekil 48) araliklari, dört model arasinda karsilastirilmistir. NNET kararli kalmistir ve duyarlilik ve özgüllük açisindan diger üç modelden büyük ölçüde daha iyi performans göstermistir. Duyarlilik ve Özgüllük ortalama degeri 0.84'ten büyüktür (Tablo 2). Ayrica, dört model arasinda dogruluk ve AUC karsilastirildiginda, NNET, daha yüksek bir performans elde etmistir. Alici isletim özelligi (ROC) egrisi ve egri altinda kalan alan (AUC), yüksek risk grubuna yönelik 0.9, orta risk grubuna yönelik geçerlilik). Bu NNET modeline follistatin eklenmesi, yüksek risk grubunun AUC'sini önemli ölçüde gelistirmistir (AUC 0.9 , P = 4e-04 DeLong testi). Orta riske yönelik, AUC'Ier bu sekilde gelismisken (AUC 0.99 , P = 19-02 DeLong testi), düsük riske yönelik sirasiyla bu sekildedir (AUC 0.96 , P = 1e- 01 DeLong testi), (Tablo 3 ve Sekil 5). NNET, SVM, RF and GLM machine learning methods with 10x cross validation evaluated (Table 1). Sensitivity, specificity, accuracy, and AUC (Figure 4A) ranges and confidence intervals (Figure 48) were compared between the four models. NNET remained stable and greatly outperformed the other three models in sensitivity and specificity. has performed better. Sensitivity and Specificity mean value from 0.84 is large (Table 2). Also, when comparing the accuracy and AUC between the four models, NNET has achieved a higher performance. Receiver operating characteristic (ROC) curve and area under the curve (AUC) 0.9 for high-risk group, 0.9 for intermediate-risk group validity). The addition of follistatin to this NNET model reduces the AUC of the high-risk group. significantly improved (AUC 0.9 , P = 4e-04 DeLong test). For medium risk, with AUCs developed this way (AUC 0.99 , P = 19-02 DeLong test), for low risk respectively (AUC 0.96 , P = 1e- 01 DeLong test), (Table 3 and Figure 5).
Sekil 4. Kohortta 4 yillik tip 2 diyabet insidansi riskini degerlendirmek üzere dört modelin performansi ve geçerliligi. Dört modele yönelik duyarlilik, özgüllük, dogruluk ve AUC (4A) araliklari ve güven seviyeleri (4B) gösterilir. Figure 4. Four-year risk of type 2 diabetes incidence in the cohort. model performance and validity. Sensitivity, specificity, and accuracy for the four models and AUC (4A) intervals and confidence levels (4B) are shown.
Sekil 5. 10 kat çapraz geçerlilige sahip dört biyomarkör ile seçilen modelin (NNET) ROC egrisi. ROC egrileri, kohort veri setinde (10 kat çapraz geçerlilik) diyabet risk gruplari (yüksek, orta ve düsük risk) üzerinde dört biyomarkör (baslangiç, follistatin, proinsülin ve C-peptidin HbAic'i) içeren NNET modeline yönelik sunulur. Follistatin içeren ve içermeyen ROC imza egrilerine yönelik DeLong testi, yüksek risk grubuna yönelik p:4e-04, orta risk grubuna yönelik p=0.01 ve düsük risk grubuna yönelik p=0.1'dir. Figure 5. Model selected (NNET) with four biomarkers with 10-fold cross validation. ROC curve. ROC curves, diabetes risk in cohort dataset (10-fold cross-validation) four biomarkers (baseline, follistatin, proinsulin and HbAic of C-peptide) are presented for the NNET model. containing follistatin DeLong test for ROC signature curves with and without, for high-risk group p:4e-04 is p=0.01 for medium risk group and p=0.1 for low risk group.
Tartisma üzere bir biyomarkör imzasi olusturmak üzere dört degisken kullandik: baslangiç follistatin, HbA1c. pro-insülin ve C-Peptid. Çoklu istatistiksel yaklasimlar kullanilarak dört kan biyomarkör tarafindan tip 2 diyabet riskini degerlendirmek üzere bir çoklu biyomarkör modeli gelistirilmistir. NNET modelinin performansi, diger tüm baslangiç risk ölçümlerinkinden daha iyidir. Bu NNET modeli, bir risk tahmini elde etmek üzere daha uygun bir alternatif saglar: bir laboratuvar, açlik kan numunesindeki biyomarkör konsantrasyonlarini ölçer ve hesaplanan risk seviyesini döndürür. Bu NNET modeli, antropometrilere veya kisinin bildirdigi risk faktörlerine (aile öyküsü veya tütün kullanimi gibi) bagli degildir. Argument We used four variables to generate a biomarker signature: initial follistatin, HbA1c. pro-insulin and C-Peptide. Type 2 diabetes by four blood biomarkers using multiple statistical approaches A multi-biomarker model has been developed to assess risk. of the NNET model performance is better than that of any other initial risk measure. This NNET model is a provides a more convenient alternative to obtaining a risk estimate: a laboratory, fasting blood measures the biomarker concentrations in the sample and calculates the calculated risk level. returns. This NNET model is based on anthropometrics or self-reported risk factors (family history or tobacco use).
NNET modeline yönelik seçilen dört biyomarkör, çesitli biyolojik yollarda yer alir. Pro- insülin, diyabet ve obezite dahil olmak üzere metabolik bozukluklarin kritik göstergeleridir. Insülinin prekürsörü olan Pro-insülinin orantisiz salgilanmasinin, yalnizca insülin direncinin spesifik bir göstergesi degil, ayni zamanda ß-hücresi islev bozuklugununlg] da bir özelligi olabilecegi gösterilmistir. Follistatin, hemen hemen tüm ana dokularda eksprese edilen salgilanan bir proteindir ve çalismalar, follistatinin, tip 2 diyabetlilm] hastalarda yüksek plazma seviyeleri ile metabolik hastaliklarÜO- ”1 ile baglantili oldugunu öne sürmüstür. Dolasimdaki follistatin, insülini artirarak ve pankreastanm] glukagon salgilanmasini baskilayarak insanlarda glikoz metabolizmasi üzerinde dogrudan etkilere sahiptir. Ancak follistatinin mevcut tarifnamede gösterildigi gibi tip 2 diyabet baslangicindan önce tip 2 diyabet insidansini tahmin edip etmedigi önceden bilinmiyordu. The four selected biomarkers for the NNET model are involved in various biological pathways. Pro- insulin is critical for metabolic disorders, including diabetes and obesity. are indicators. Disproportionate secretion of Pro-insulin, the precursor of insulin, not only a specific indicator of insulin resistance, but also ß-cell function It has been shown that the disorder may also have a feature. Follistatin, almost all is a secreted protein expressed in major tissues and studies have shown that follistatin, type 2 diabetes, metabolic diseases with elevated plasma levels in patients with UO-”1 He claimed it was connected. Circulating follistatin, by increasing insulin and glucose metabolism in humans by inhibiting glucagon secretion from the pancreas has direct effects on However, as follistatin is shown in the present specification whether it predicts the incidence of type 2 diabetes before the onset of type 2 diabetes, such as was previously unknown.
Diyabetik farelerin pankreasinda follistatinin lokal asiri ekspresyonu, artan serum insülin seviyeleri ile sonuçlanmistirllsl. Tao et al. tarafindan yakin zamanda yapilan bir çalisma, follistatini diyabetm] ile iliskili sistemik metabolik düzensizligin bir aracisi olarak tanimlamistir. Hiperglisemik farelerde ve yüksek yagli beslenen obez farelerde, follistatinin parçalanmasi, glikoz toleransini, beyaz yag dokusu insülin sinyalini ve insülin tarafindan hepatik glikoz üretiminin baskilanmasini restore etmistir. Önceden, karacigerden follistatin salgilanmasinin, bu tarifnamede gösterildigi gibi glukagon ve insülin ile birlikte GCKR tarafindan düzenlendigi bilinmiyordu (Sekil 1). Local overexpression of follistatin in the pancreas of diabetic mice, increased serum insulin levels resulted. According to Tao et al. A recent study by follistatin as a mediator of systemic metabolic dysregulation associated with diabetes]. has been defined. In hyperglycemic mice and high-fat-fed obese mice, degradation of follistatin, glucose tolerance, white adipose tissue insulin signaling and insulin It restored the suppression of hepatic glucose production by Previously, follistatin secretion from the liver, glucagon and It was not known to be regulated by GCKR together with insulin (Fig. 1).
Gastrik bypass cerrahisi geçiren diyabetli obez bireylerde, serum follistatin, Hbch seviyelerine paralel olarak azalmistir. HbA1C, öncelikle üç aylik ortalama plazma-glikoz konsantrasyonunu tanimlamak üzere ölçülür ve bu nedenle diyabete yönelik bir teshis testi olarak kullanilabilir. In diabetic obese individuals undergoing gastric bypass surgery, serum follistatin, Hbch decreased in parallel with the levels. HbA1C, primarily the three-month average plasma-glucose It is measured to identify the concentration of diabetes and therefore a diagnosis of diabetes can be used as a test.
Gestasyonel diyabet öyküsü olan Çinli kadinlarda serum C-peptid seviyeleri ile diyabet ve prediyabet riskleri arasinda pozitif bir iliski oldugu bulunmustur. Önceki bulgu, yüksek C-peptid seviyelerinin diyabet ve prediyabetinlw] bir göstergesi olabilecegini öne sü rm üstü r. Diabetes mellitus with serum C-peptide levels in Chinese women with a history of gestational diabetes. It has been found that there is a positive relationship between the risk of diabetes and prediabetes. previous finding, high It is suggested that C-peptide levels may be an indicator of diabetes and prediabetes. over version r.
Degiskenler, makine ögrenimi yöntemleri kullanilarak birkaç matematiksel modelde çalistirilmistir: SVM, NNET, RF ve GLM. Test edilen tüm yöntemler arasinda en iyi performansi NNET vermistir. NNET, seçilen biyomarkör imzasini kullanarak, bireyin dört yil içinde diyabet gelistirme riskinin düsük, orta veya yüksek olup olmadigini çok yüksek özgüllük ve duyarlilikla tahmin eder. AUC, yüksek riski tahmin etmek üzere 0.9 (10 kat çapraz geçerlilik), orta riski tahmin etmek üzere 0.99 ve düsük riski tahmin etmek üzere 0.96'dir (Sekil 3). AUC'nin follistatin içeren ve içermeyen model arasinda karsilastirilmasi, çoklu biyomarkörlerin, tek biyomarkörle ve follistatin içermeyenlerden daha iyi performans gösterdigini göstermistir. Özetle, biyomarkör seçimine yönelik çesitli istatistiksel yöntemler uygulayarak, dört adede kadar dolasimdaki biyomarkör içeren bir NNET modeli gelistirdik. Bu NNET, tek basina tek biyomarkör ve Follistatin içermeyen model ile karsilastirildiginda diyabet riskinin daha iyi degerlendirilmesini saglar. Mevcut sonuçlar, bu NNET modelinin, en kapsamli önleme stratejilerinin göz önünde bulundurulmasi gereken bir popülasyon olan tip 2 diyabet gelistirme riski en yüksek olan bireyleri tanimlamaya yönelik önemli bir araç olabilecegini ileri sürer. Bu modelin tek markörler ile karsilastirildiginda gelistirilmis performansi, tip 2 diyabet ile iliskili çesitli patofizyolojik yollardan Follistatin dahil olmak üzere çoklu biyomarkörleri içeren risk degerlendirme modellerinin degerini gösterir.The variables are represented in several mathematical models using machine learning methods. has been run: SVM, NNET, RF and GLM. Best among all tested methods NNET gave the performance. NNET, using the selected biomarker signature, very high whether the risk of developing diabetes during the year is low, moderate, or high predicts with specificity and sensitivity. AUC is 0.9 (10-fold) to estimate high risk. cross validation), 0.99 to estimate medium risk and 0.99 to estimate low risk 0.96 (Figure 3). Between the follistatin and non-follistatin model of AUC Comparison of multiple biomarkers with a single biomarker and those without follistatin showed better performance. In summary, by applying various statistical methods for biomarker selection, four We developed an NNET model with up to 2 circulating biomarkers. This is NNET, the only Diabetes mellitus compared with a single biomarker per and Follistatin-free model enables better assessment of risk. The current results show that this NNET model is the most population for which comprehensive prevention strategies should be considered. An important tool for identifying individuals at highest risk of developing type 2 diabetes claims it can. Improved comparison of this model with single markers performance of a variety of pathophysiological pathways associated with type 2 diabetes, including Follistatin. It indicates the value of risk assessment models that include multiple biomarkers, such as
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