Risk assessment for progression of Diabetic Nephropathy based on patient history analysis

A nefropatia diabética (ND) é uma das complicações mais comuns em doentes com diabetes. Trata-se de uma doença crónica que afeta progressivamente os rins, podendo resultar numa insuficiência renal. A digitalização permitiu aos hospitais armazenar as informações dos doentes em registos de saúde eletrónicos (RSE). A aplicação de algoritmos de Machine Learning (ML) a estes dados pode permitir a previsão do risco na evolução destes doentes, conduzindo a uma melhor gestão da doença. O principal objetivo deste trabalho é criar um modelo preditivo que tire partido do historial do doente presente nos RSE. Foi aplicado neste trabalho o maior conjunto de dados de doentes portugueses com DN, seguidos durante 22 anos pela Associação Protetora dos Diabéticos de Portugal (APDP). Foi desenvolvida uma abordagem longitudinal na fase de pré-processamento de dados, permitindo que estes fossem servidos como entrada para dezasseis algoritmos de ML distintos. Após a avaliação e análise dos respetivos resultados, o Light Gradient Boosting Machine foi identificado como o melhor modelo, apresentando boas capacidades de previsão. Esta conclusão foi apoiada não só pela avaliação de várias métricas de classificação em dados de treino, teste e validação, mas também pela avaliação do seu desempenho por cada estádio da doença. Para além disso, os modelos foram analisados utilizando gráficos de feature ranking e através de análise estatística. Como complemento, são ainda apresentados a interpretabilidade dos resultados através do método SHAP, assim como a distribuição do modelo utilizando o Gradio e os servidores da Hugging Face. Através da integração de técnicas ML, de um método de interpretação e de uma aplicação Web que fornece acesso ao modelo, este estudo oferece uma abordagem potencialmente eficaz para antecipar a evolução da ND, permitindo que os profissionais de saúde tomem decisões informadas para a prestação de cuidados personalizados e gestão da doença

Common gene signatures and molecular mechanisms of diabetic nephropathy and metabolic syndrome

BackgroundDiabetic nephropathy (DN) is the leading cause of end-stage renal disease. Multiple metabolic toxicities, redox stress, and endothelial dysfunction contribute to the development of diabetic glomerulosclerosis and DN. Metabolic syndrome (MetS) is a pathological state in which the body’s ability to process carbohydrates, fats, and proteins is compromised because of metabolic disorders, resulting in redox stress and renal remodeling. However, a causal relationship between MetS and DN has not been proven. This study aimed to provide valuable information for the clinical diagnosis and treatment of MetS with DN.MethodsHere, transcriptome data of DN and MetS patients were obtained from the Gene Expression Omnibus database, and seven potential biomarkers were screened using bioinformatics analysis. In addition, the relationship between these marker genes and metabolism and immune infiltration was explored. Among the identified marker genes, the relationship between PLEKHA1 and the cellular process, oxidative phosphorylation (OXPHOS), in DN was further investigated through single-cell analysis.ResultsWe found that PLEKHA1 may represent an important biomarker that perhaps initiates DN by activating B cells, proximal tubular cells, distal tubular cells, macrophages, and endothelial cells, thereby inducing OXPHOS in renal monocytes.ConclusionOverall, our findings can aid in further investigation of the effects of drug treatment on single cells of patients with diabetes to validate PLEKHA1 as a therapeutic target and to inform the development of targeted therapies

제2형 당뇨 관련 후성유전학 지표 발굴 연구

학위논문(박사) -- 서울대학교대학원 : 보건대학원 보건학과, 2021.8. 성주헌.제2형 당뇨는 췌장의 베타 세포 (beta cell)의 기능 장애와 인슐린 저항성으로 인한 고혈당증을 특징으로 하는 만성질환이다. 한국의 제2형 당뇨 유병률은 성인의 12.4%에 달하고 있다. 제2형 당뇨는 유전적 요인과 환경적 요인, 그리고 두 요인의 상호작용으로 인해 발생한다고 알려져 있지만, 추정유전율이 25~69%에 달하는 것에 비해 전장유전체분석 (Genome-wide association study, GWAS)를 통해 확인한 변이 (genetic variants)는 추정 유전율의 일부만을 설명할 뿐이다. 이러한 관점에서 유전적 요인과 환경적 요인의 상호작용을 밝히기 위해 후성유전학적 연구가 활발히 진행되고 있다. 후성유전(Epigenetics)는 DNA 염기서열의 변화 없이 유전자 발현에 영향을 주는 현상을 의미하며, 이는 생애에서 노출되는 환경적인 요인으로 인해 영향을 받는다고 알려져 있다. 후성유전의 대표적인 기전은 DNA 메틸화 (DNAm)와 히스톤 변형 (Histone modification)이 있다. 최근 연구에서는 DNA 메틸화를 포함한 후성유전학적 변화가 제2형 당뇨 및 미세 혈관 합병증의 발병 위험에 영향을 미친다는 것이 밝혀졌다. 뿐만 아니라, 고혈당에의 노출이 DNA 메틸화의 변화를 일으키고 나아가 당뇨병성 신장질환의 위험을 증가시킨다는 ‘대사성 기억 (metabolic memory)’ 현상이 제기됨에 따라 제2형 당뇨에서의 DNA 메틸화 변화에 대한 연구의 중요성이 증가하고 있다. 따라서 본 연구는 한국인에서 제2형 당뇨와 당뇨병성 신장질환 (Diabetic kidney disease, DKD) 특이적인 DNA 메틸화 지표를 발굴하고 해당 지표들과 대사성 특성(metabolic trait)과의 연관성을 탐구하고자 수행되었다. 먼저, 전장메틸화영역 연관분석 (Methylome-wide association study)을 통해 제2형 당뇨 유병여부에 따른 DNA 메틸화 차이를 관찰하고 차이가 특이적인 지표를 발견하는 분석을 수행하였다. 해당 지표들을 이용해 DNA 메틸화 점수를 계산하였고, 이 점수에 따라 10년간 추적 관찰한 대상자들에게서 제2형 당뇨병의 발생이 차등적으로 관찰되는지에 대한 분석을 수행해 제2형 당뇨 특이적 DNA 메틸화 분석결과의 신뢰성을 확보하였다. 또한, 같은 분석방법을 사용해 당뇨병성 신장질환 특이적인 DNA 메틸화 지표를 발견하는 분석이 이어서 수행되었다. 마지막으로, 발견된 제2형 당뇨/당뇨병성 신장질환의 DNA 메틸화 마커와 대사성 특성의 연관, 인과관계를 분석해 제2형 당뇨에서의 DNA 메틸화의 기전을 밝히고자 하였다. 말초 혈액 백혈구 샘플에서 DNA를 추출한 후, KoGES 코호트의 356 명과 HTS 코호트의 일부 대상자는 Illumina 사의 Infinium HumanMethylation 450 BeadChip으로 어세이 되어 유전체 내 약 450,000 개 이상의 DNA 메틸화 위치에 대한 DNA 메틸화 수준의 데이터를 얻었으며, 일부 HTS 코호트 대상자와 서울대병원 당뇨 코호트의 대상자들은 Illumina 사의 Infinium Methylation Epic Beadchip으로 어세이되어 유전체 내 총 850,000개 이상의 DNA 메틸화 수준의 데이터를 얻을 수 있었다. 이 중 Infinium HumanMethylation 450 BeadChip으로 어세이 된 자료들은 DNA 메틸화 마커 대치 (imputation) 방법 중 하나인 ‘methylImp’를 사용하여 주변 마커들과의 상관관계를 이용한 원리로 대치되어 총 850,000개 이상의 마커를 얻을 수 있었다. 본 연구에서는 197명의 제2형 당뇨병 대상자와 232명의 비 당뇨병 대조군을 사용한 사례-대조 연구 설계 (서울대병원 당뇨 코호트)에서 전장메틸화영역연관분석을 수행하였다. 제2형 당뇨병 그룹은 87명의 당뇨병성 신장질환 대상자와 80명의 비당뇨병성 신장질환군 (제2형 당뇨 유병 기간 10년 이상의 환자로 환장)으로 세분화되었다. 추가적으로 한국인 가족-쌍둥이 (HTS) 코호트 및 한국인유전체역학조사사업 (KoGES) 코호트의 2 개의 인구 기반 코호트에서 추가로 819 명의 대상자를 사용하여 발견된 DNA 메틸화 지표와 대사성 특성의 연관성을 조사하였다. 대사성 특성과 DNA 메틸화 지표의 인과 관계를 조사하기 위해 멘델리안 무작위 분석방법 (Mendelian randomization, MR)이 적용되었다. 본 연구에서는 제2형 당뇨의 8 개의 차등메틸화 영역 (Differentially methylated site, DMS) (각각 BMP8A, NBPF20, STX18, ZNF365, CPT1A, TRIM37 및 TXNIP에서 2 개)을 DNA 메틸화연구에서의 유의성 임계값(P <9.0x10-8)에서 확인했으며 이 중 TXNIP과 CPT1A에 위치한 3 개의 차등메틸화 영역은 기존 연구에서 확인된 영역이었다. 8개의 DNA 메틸화 마커로 구성된 DNAm 점수를 개인별로 계산하였으며, DNAm 점수의 1분위와 10분위의 상대적 위험이 2.86 (95% 신뢰구간 1.10-7.44) 으로 나타났다. 전장메틸화연관분석이 수행된 것과 독립적인 전향 코호트 (KoGES)에서 제2형 당뇨병 발병 위험을 예측할 수 있어, 발견된 제2형 당뇨 특이적인 DNA 메틸화 마커의 신뢰성을 확보할 수 있었다. 멘델리안 무작위분석 방법을 이용하여 제2형 당뇨와 관련된 차등메틸화영역은 당뇨병 발병에 인과적인 효과가 없다는 것을 확인하였다. 이 과정에서 이용된 내생변수로 DNA 메틸화 정량 유전자좌 (methylation quantitative loci, mQTL)를 사용하였는데, 이 중 선행된 전장유전체 분석에서 보고된 제2형 당뇨와 대사특성 관련 단일염기다형성 (Single nucleotide polymorphism, SNP)이 발견되었다. 이는 해당 DNA 메틸화 정량 유전자좌와 관련이 있는 차등메틸화영역과 제2형 당뇨의 연관성에 교란효과가 있을 수 있음을 시사한다. CPT1A 및 TXNIP의 DMR은 공복 혈당, HbA1c 및 체질량 지수를 포함한 정량적 대사 특성과 관련이 있었으며, 특히 CPT1A의 DNA 메틸화는 공복 혈당에 인과적으로 조절되는 것으로 보였다. 또한 167명의 당뇨병성 신장 대상자 및 비 당뇨병성신증 대조군의 전장메틸화연관분석 연구에서 당뇨병성 신장질환과 관련된 3 개의 차등메틸화 영역(COMMD1, TMOD1 및 FHOD1)을 확인할 수 있었다. 당뇨병성 신장질환의 차등메틸화영역은 제2형 당뇨병에서 발견된 차등메틸화영역과 통계적으로 의미 있는 수준에서 공통적인 부분을 발견할 수 없었다. 당뇨병성 신장질환의 차등메틸화 영역은 유전변이에 의해 많은 부분이 설명 되고 있었으며, 멘델리안 무작위분석방법을 통해 당뇨병성 신장질환에 인과적인 영향을 미치고 있음을 확인할 수 있었다. 추정된 사구체 여과율 (eGFR)은 당뇨병성 신장질환에서 발견된 3개의 차등메틸화 영역과 인과 관계가 관찰되었다. 결론적으로, 우리는 동아시아 인구에서 제2형 당뇨병과 관련된 8 개의 차등메틸화영역을 메틸화연구에서의 유의성 임계값 (P <9.0x10-8)에서 발견하였으며 이 중 5개는 본 연구에서 새롭게 밝혀진 DNA 메틸화 지표이다. 또한 당뇨병성 신장질환과 관련된 3개의 차등메틸화영역을 확인하였으며, 이는 제2형 당뇨의 영역과는 공통적인 부분이 발견되지 않았다. 본 연구 결과는 당뇨병성 신장질환의 후성유전학적 기전이 제2형 당뇨의 기전과는 다를 것임을 시사한다.Objective: There is growing body of evidence that epigenetic changes including DNA methylation (DNAm) influence the risk of type 2 diabetes and its microvascular complications. We conducted a methylome-wide association study (MWAS) to identify differentially methylated regions (DMRs) of type 2 diabetes and diabetic kidney disease (DKD) in Korean population. Methods: We performed an initial MWAS in 232 participants in a case-control study design with type 2 diabetes and 197 non-diabetic controls with Illumina EPIC bead chip using peripheral blood leukocytes. Type 2 diabetes group was subdivided to 87 DKD cases and 80 non-DKD controls. Additional 819 individuals from two population-based cohorts were used to investigate the association of the identified DMRs with quantitative metabolic traits. We developed a DNAm score using identified DMRs to predict the occurrence of type 2 diabetes. To examine the causal relationship between differentially methylated CpGs and type 2 diabetes/DKD and between the metabolic traits and differentially methylated status, we performed Mendelian randomization (MR) analyses. Results: We identified eight DMRs (each at BMP8A, NBPF20, STX18, ZNF365, CPT1A, and TRIM37, and two at TXNIP) which were significantly associated with risk of type 2 diabetes (P < 9.0×10-8), including three that were previously known (DMRs in TXNIP and CPT1A), in 429 type 2 diabetes cases and controls. DNAm score consisted of these DMRs differentiated the risk of developing type 2 diabetes in an independent prospective cohort with a relative risk of 2.86 (95% CI 1.10-7.44) between the lowest and highest deciles of DNAm score. DMRs in CPT1A and TXNIP were associated with quantitative metabolic traits, including fasting glucose, HbA1c, and body mass index. DMRs of type 2 diabetes have little methylation quantitative loci (mQTL), and not causally associated with type 2 diabetes. Three DMSs of type 2 diabetes (cg26823705, cg08867893, and cg17082373) affect the increase of HbA1c level. We also identified three DMRs (on COMMD1, TMOD1, and FHOD1) associated with DKD in 167 DKD cases and controls. DMRs of DKD did not show meaningful overlap with those of type 2 diabetes. DKD was causally affected by methylation changes of DMRs of DKD, which was highly influenced by genetics. In MR analysis, the estimated glomerular filtration rate was causally associated with DNAm of these three DMRs. Conclusions: In an East Asian population, we identified eight DMRs, including five novel ones, associated with type 2 diabetes and three DMRs associated with DKD at methylome-wide statistical significance. Our findings suggest that epigenetic machinery of DKD may be different from that are responsible for the development of T2D.I. Introduction: Epigenetics, Epigenetic markers and Type 2 diabetes 11 1. Overview of epigenetics 11 1.1. Epigenetics and DNA methylation 11 1.2. Environmental exposures trigger epigenetic change 12 1.3. DNA methylation profiling 13 1.4. Characteristic of DNA methylome data for Methylome-wide association study (MWAS) 14 2. Epigenetics and type 2 diabetes 16 2.1. Epidemiology of Type 2 diabetes 16 2.2. DNAm changes in type 2 diabetes 17 2.3. Metabolic memories of diabetic complication, DKD 18 2.4. Genetic influences on DMRs 19 3. Imputation of DNA methylation markers 21 3.1. Review of DNA methylation imputation methods 21 3.2. Imputation of DNA methylation population datasets 24 4. Objectives 26 II. MWAS of type 2 diabetes and Verifying markers 28 1. Material and methods 28 1.1. Study design and population 28 1.2. DNA methylation profiling 29 1.3. Removing Batch effect 30 1.4. MWAS of type 2 diabetes 31 1.5. Prediction of type 2 diabetes using the DNAm score 32 1.6. Prediction of type 2 diabetes prevalence using the DNA methylation 32 1.7. Causal analysis of DNA methylation to Type 2 diabetes 33 1.8. The phenotypic variance explained by DNA methylation 34 2. Results 35 2.1. Clinical characteristics of the study participants 35 2.2. Differentially methylated regions of type 2 diabetes 35 2.3. Prediction of the risk of type 2 diabetes using the DNAm score 36 2.4. Prediction of prevalence of type 2 diabetes using the DNA methylation 37 2.5. The causal effect of DNA methylation on type 2 diabetes 37 2.6. The phenotypic variance explained by DNA methylation 38 3. Discussion 38 III. MWAS of Diabetic Complication, DKD 70 1. Method and materials 70 1.1. Study design and participants 70 1.2. MWAS of DKD 70 1.3. The causal effect of DNA methylation on DKD 71 2. Results 73 2.1. Clinical characteristics of the study participants 73 2.2. Differentially methylated regions of DKD 73 2.3. The causal effect of DNA methylation on DKD 74 3. Discussion 75 IV. Causal Analysis of Differentially Methylated Regions Associated with type 2 diabetes/DKD 100 1. Methods and materials 100 1.1. Study participants 100 1.2. DNA profiling 100 1.3. Genotyping the data 100 1.4. Association with quantitative metabolic traits 101 1.5. MR analysis 102 2. Results 103 2.1. Association of identified DMSs with metabolic traits 103 2.2. Causal association of metabolic traits and type 2 diabetes/DKD with CpG methylation 104 2.3. Causal association of CpG methylation with metabolic traits and type 2 diabetes/DKD 105 3. Discussion 106 V. Overall summary and Conclusion 144 VI. References 152 VII. 국문초록 160박

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

Background and objective Type 2 diabetes mellitus (T2DM) complications seriously affect the quality of life and could not be cured completely. Actions should be taken for prevention and self-management. Analysis of warning factors is beneficial for patients, on which some previous studies focused. They generally used the professional medical test factors or complete factors to predict and prevent, but it was inconvenient and impractical for patients to self-manage. With this in mind, this study built a Bayesian network (BN) model, from the perspective of diabetic patients’ self-management and prevention, to predict six complications of T2DM using the selected warning factors which patients could have access from medical examination. Furthermore, the model was analyzed to explore the relationships between physiological variables and T2DM complications, as well as the complications themselves. The model aims to help patients with T2DM self-manage and prevent themselves from complications. Methods The dataset was collected from a well-known data center called the National Health Clinical Center between 1st January 2009 and 31st December 2009. After preprocess and impute the data, a BN model merging expert knowledge was built with Bootstrap and Tabu search algorithm. Markov Blanket (MB) was used to select the warning factors and predict T2DM complications. Moreover, a Bayesian network without prior information (BN-wopi) model learned using 10-fold cross-validation both in structure and in parameters was added to compare with other classifiers learned using 10-fold cross-validation fairly. The warning factors were selected according the structure learned in each fold and were used to predict. Finally, the performance of two BN models using warning features were compared with Naïve Bayes model, Random Forest model, and C5.0 Decision Tree model, which used all features to predict. Besides, the validation parameters of the proposed model were also compared with those in existing studies using some other variables in clinical data or biomedical data to predict T2DM complications. Results Experimental results indicated that the BN models using warning factors performed statistically better than their counterparts using all other variables in predicting T2DM complications. In addition, the proposed BN model were effective and significant in predicting diabetic nephropathy (DN) (AUC: 0.831), diabetic foot (DF) (AUC: 0.905), diabetic macrovascular complications (DMV) (AUC: 0.753) and diabetic ketoacidosis (DK) (AUC: 0.877) with the selected warning factors compared with other experiments. Conclusions The warning factors of DN, DF, DMV, and DK selected by MB in this research might be able to help predict certain T2DM complications effectively, and the proposed BN model might be used as a general tool for prevention, monitoring, and self-management

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Elucidating causal relationships between energy homeostasis and cardiometabolic outcomes

Energy metabolism dyshomeostasis is associated with multiple health problems. For example, abundant epidemiological data show that obesity and overweight increase the risk of cardiometabolic diseases and early mortality. Type 2 diabetes (T2D), characterized by chronically elevated blood glucose, is also associated with debilitating complications, high healthcare costs and mortality, with cardiovascular complications accounting for more than half of T2D-related deaths. Prediabetes, which is defined as elevated blood glucose below the diagnostic threshold for T2D, affects approximately 350M people worldwide, with about 35-50% developing T2D within 5 years. Further, non-alcoholic fatty liver disease, a form of ectopic fat deposition as a result of energy imbalance, is associated with increased risk of T2D, CVD and hepatocellular carcinoma. Determination of causal relationships between phenotypes related to positive energy balance and disease outcomes, as well as elucidation of the nature of these relationships, may help inform public health intervention policies. In addition, utilizing big data and machine learning (ML) approaches can improve prediction of outcomes related to excess adiposity both for research purposes and eventual validation and clinical translation. AimsIn paper 1, I set out to summarize observational evidence and further determine the causal relationships between prediabetes and common vascular complications associated with T2D i.e., coronary artery disease (CAD), stroke and renal disease. In paper 2, I studied the association between LRIG1 genetic variants and BMI, T2D and lipid biomarkers. In paper 3, we used ML to identify novel molecular features associated with non-alcoholic fatty liver disease (NAFLD). In paper 4, I elucidate the nature of causal relationships between BMI and cardiometabolic traits and investigate sex differences within the causal framework.ResultsPrediabetes was associated with CAD and stroke but not renal disease in observational analyses, whilst in the causal inference analyses, prediabetes was only associated with CAD. Common LRIG1 variant (rs4856886) was associated with increased BMI and lipid hyperplasia but a decreased risk of T2D. In paper 3, models using common clinical variables showed strong NAFLD prediction ability (ROCAUC = 0.73, p < 0.001); addition of hepatic and glycemic biomarkers and omics data to these models strengthened predictive power (ROCAUC = 0.84, p < 0.001). Finally, there was evidence of non-linearity in the causal effect of BMI on T2D and CAD, biomarkers and blood pressure. The causal effects BMI on CAD were different in men and women, though this difference did no hold after Bonferroni correction. ConclusionWe show that derangements in energy homeostasis are causally associated with increased risk of cardiometabolic outcomes and that early intervention on perturbed glucose control and excess adiposity may help prevent these adverse health outcomes. In addition, effects of novel LRIG1 genetic variants on BMI and T2D might enrich our understanding of lipid metabolism and T2D and thus warrant further investigations. Finally, application of ML to multidimensional data improves prediction of NAFLD; similar approaches could be used in other disease research

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays