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

Quality of care in incident type 2 diabetes and initial presentation of vascular complications: Prospective cohort study using linked electronic health records from CALIBER research platform

Background. Numbers of new cases of type 2 diabetes (T2D) are increasing rapidly. Early and continuing intervention after T2D presentation is crucial for best possible outcomes, ensuring that the existing high burden of T2D will not be aggravated. Identification of patterns of continuous care and predictors for meeting key targets for T2D management can improve quality of care. Glycaemic control is particularly important for primary prevention of vascular complications but its relationship with contemporary cardiovascular diseases (CVDs) has been less explored. More importantly, long-term glycaemic control can be assessed from routine monitoring, potentially providing new insight into T2D management to prevent vascular complications. Linked electronic health records are invaluable data resources for investigating these issues. Objective. To examine the quality of care in an incident T2D cohort through assessment of temporal trends of care, predictors of glycaemic, blood pressure and lipid control, and associations of short-term and long-term glycaemic control with chronic vascular complications. Methods. The data source for studies in this thesis was CALIBER which links electronic health records from primary care, hospitalisation, myocardial infarction and mortality registries. Patients newly diagnosed with T2D between 1998 and 2010 were followed-up until a censoring administrative date or initial occurrence of vascular complications. Trends in receipt of care and attainment of glycaemic, blood pressure and total cholesterol targets were examined. Predictors for meeting the targets were explored using multinomial logistic regressions. Association of early glycaemic control with a range of specific cardiovascular complications were investigated using Cox regressions. A longitudinal metric for glycaemic control was developed by quantifying time spent at target during follow-up and was tested for its association with cardiovascular and microvascular outcomes using mixed logistic regressions. Results. A total of 52,379 incident T2D patients were identified with a median follow-up of over 4 years. Positive trends were observed for blood pressure and total cholesterol control, but not for glycaemic control, whilst attainment of HbA1c and blood pressure targets over time consistently fell short. Older age at diagnosis was an important predictor for meeting the key targets. In 36,149 patients free from prior CVD, early glycaemic and blood pressure control was associated with lower risk for heart failure and peripheral arterial disease, whereas cholesterol control with myocardial infarction and transient ischaemic attack. Shorter duration at glycaemic target was associated with higher risk of major adverse cardiovascular events, cardiovascular death and diabetic retinopathy. Conclusions. This thesis highlights missed opportunities and inequality in T2D care. Both short-term and long-term glycaemic control are important for reducing risk of vascular complications. Limitations and implications of the findings for clinical practice and research were discussed

Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury

The increasing use of electronic platforms in healthcare has resulted in the generation of unprecedented amounts of data in recent years. The amount of data available to clinical researchers, physicians, and healthcare administrators continues to grow, which creates an untapped resource with the ability to improve the healthcare system drastically. Despite the enthusiasm for adopting electronic health records (EHRs), some recent studies have shown that EHR-based systems hardly improve the ability of healthcare providers to make better decisions. One reason for this inefficacy is that these systems do not allow for human-data interaction in a manner that fits and supports the needs of healthcare providers. Another reason is the information overload, which makes healthcare providers often misunderstand, misinterpret, ignore, or overlook vital data. The emergence of a type of computational system known as visual analytics (VA), has the potential to reduce the complexity of EHR data by combining advanced analytics techniques with interactive visualizations to analyze, synthesize, and facilitate high-level activities while allowing users to get more involved in a discourse with the data. The purpose of this research is to demonstrate the use of sophisticated visual analytics systems to solve various EHR-related research problems. This dissertation includes a framework by which we identify gaps in existing EHR-based systems and conceptualize the data-driven activities and tasks of our proposed systems. Two novel VA systems (VISA_M3R3 and VALENCIA) and two studies are designed to bridge the gaps. VISA_M3R3 incorporates multiple regression, frequent itemset mining, and interactive visualization to assist users in the identification of nephrotoxic medications. Another proposed system, VALENCIA, brings a wide range of dimension reduction and cluster analysis techniques to analyze high-dimensional EHRs, integrate them seamlessly, and make them accessible through interactive visualizations. The studies are conducted to develop prediction models to classify patients who are at risk of developing acute kidney injury (AKI) and identify AKI-associated medication and medication combinations using EHRs. Through healthcare administrative datasets stored at the ICES-KDT (Kidney Dialysis and Transplantation program), London, Ontario, we have demonstrated how our proposed systems and prediction models can be used to solve real-world problems

약동학/약력학 모델을 이용한 요독증의 evogliptin 약동학에 대한 영향 평가

학위논문(박사) -- 서울대학교대학원 : 의과대학 협동과정 임상약리학전공, 2023. 8. 조주연.서론: 요독증(uremia) 또는 요독증후군(uremic syndrome)은 신기능 저하로 인해 혈액 중 노폐물(요독소)이 축적되는 병리학적 상태이다. 요독소는 몸에 축적되어 사이토크롬 P450 효소(CYP3A4 등)를 통해 이루어지는 약물 대사 및 배설과 같은 여러 생리 과정에 영향을 준다. 에보글립틴(evogliptin)은 2형 당뇨병 치료에 사용되는 디펩티딜 펩티다제-4(DPP-4) 억제제로, 주로 간에서 CYP3A4 효소에 의해 대사된다. 요독증은 CYP3A4의 기능에 영향을 미칠 수 있으며, 이는 에보글립틴의 대사 및 배설에 중요한 영향을 미칠 수 있다. 신장 손상 환자에서 CYP3A4를 주로 대사시키는 에보글립틴과 같은 약물에 대한 인구 약동학(PK) 및 약력학(PD) 모델링을 수행하면, CYP3A4를 주로 대사시키는 약물의 약동학을 예측할 수 있다. 본 연구는 다양한 정도 신장 질환을 가진 환자에서 에보글립틴의 인구 PK 및 PD 모델을 구축하는 것을 목표로 하였다. 방법: 본 연구에서는 에보글립틴의 두 가지 임상 연구 데이터를 사용하였다. 하나는 다양한 정도의 신장 손상 환자와 정상 신장 기능을 가진 환자를 대상으로 한 임상 연구(NCT02214693)이고, 다른 하나는 말기 신장 질환(ESRD) 환자와 정상 신장 기능을 가진 환자를 대상으로 한 단회 투여 연구(NCT04195919)이다. 두 연구에서 대상자들은 공복 상태에서 5mg의 에보글립틴을 투여 받았다. 총 46명의 대상자로부터 취득한 688건의 에보글립틴 농도 및 598건의 DPP-4 활성 데이터가 분석에 사용되었다. 에보글립틴의 PK/PD 데이터와 혈액학, 혈액화학, 인구통계학 데이터 등의 잠재적 공변량 정보를 사용하여 인구 PK/PD 모델을 구축하였다. 모델 구축에는 비선형 혼합효과 모델링 소프트웨어(NONMEM® 버전 7.4)를 사용하였으며, 일차 조건부 추정과 상호 작용(FOCE-I)을 이용하였다. 각 매개변수는 전진 선택 및 후진 제거 방식을 사용하여 구조 모델에 순차적으로 추가되었으며, 각각 0.01과 0.001의 유의 수준을 적용하였다. 비모수적 부트스트랩 재표본 추출법을 사용하여 모델의 안정성과 모델 매개변수의 신뢰구간(CI)을 평가하였으며, 데이터 세트의 부트스트랩 복제본(n=1000)에 대해 최종 모델을 반복적으로 적용하였다. 예측 수정 시각 예측 검증(pcVPCs; 500회 시뮬레이션 복제본)을 수행하여 최종 모델을 검증하였다. 최종 PK 모델을 사용하여 5 mg 단회 투여를 가정한 농도-시간 곡선 하 면적(AUC) 및 최대 혈장 농도(Cmax)를 계산하였으며, 다양한 정도의 신장 기능을 가진 환자들의 시나리오를 평가하였다. 결과: 다양한 정도의 신장 손상을 가진 환자와 건강한 대상자들로 구성된 총 46명의 참여자가 연구에 참여하였다. 각 연구의 환자 그룹은 인구통계학적 특성이 비슷했으며, 환자군의 신장 손상의 정도는 달랐다. 총 688개의 혈장 PK 샘플을 사용하여 에보글립틴의 인구 약동학(PK)을 설명하는 비선형 혼합효과 모델을 개발하였다. 아카이케 정보 기준(AIC), 각종 진단 플롯 및 목표 함수 값(OFV)에 기반하여 2-구획 모델과 일차 흡수가 선택된 기본 PK 모델이 선택되었다. 기본 PK 모델은 신뢰할 수 있는 매개변수 추정 및 관찰된 데이터와 예측된 데이터 간의 강한 일치성을 보였다. 최종 모델에 유지된 중요한 공변량은 혈중 chloride 및 amylase 수치가 Fr(상대적 생체이용률)에, 나이가 CL/F (외적 청소율)에, 그리고 체중이 V3/F (말초 분포량)에 영향을 미쳤다. pcVPC는 시뮬레이션된 에보글립틴 농도와 관찰된 농도 간의 중첩을 보여주었으며, 부트스트랩은 1000회 복제본 중 93.1%의 성공률을 보였다. 에보글립틴의 약동학에 관련된 공변량의 잠재적 영향을 몬테카를로 시뮬레이션을 통해 평가하였다. 시뮬레이션 결과와 이전에 보고된 evogliptin의 PK 데이터를 종합하였을 때, 중증 신장 기능 저하 환자에서 초회통과효과 대사 억제가 유의미하게 나타나는 것이 예측되었다. Direct link sigmoidal Emax 모델을 개발하여 혈장 evogliptin 농도와 DPP-4 억제 간의 관계를 설명했다. Evogliptin의 PK/PD 모델은 최대 효과 시에 DPP-4의 거의 완전한 억제를 예측하였으며 (Emax: 95.7%), 낮은 EC50 값 (0.837 μg/L)을 보여주어 evogliptin의 높은 효력과 효능을 나타내었다. 결론: 개발된 에보글립틴의 PK/PD 모델은 신장 기능 저하가 있는 개체에서 흡수, 체내 노출, 배설 변동성을 정확하게 예측하였다. 본 연구는 신장 손상의 정도가 CYP3A4를 통해 대사되는 약물의 상대 생체이용률에 영향을 줄 수 있음을 시사한다. 이 모델은 앞으로 신장 기능 저하 환자에서 비 신장 약물 청소에 대한 요독증의 영향을 평가하는 근거로 사용될 수 있으며, 신장 기능 저하 환자를 위한 용량 조정 방안을 최적화하는 데 도움을 주리라 판단된다.Introduction: Uremia, also known as uremic syndrome, is a pathological condition characterized by the retention of waste products (uremic toxins) in the blood due to inadequate kidney function. Uremic toxins can accumulate in the body and affect various physiological processes, including drug metabolism and elimination mediated by cytochrome P450 enzymes such as CYP3A4. Evogliptin is a dipeptidyl peptidase-4 (DPP-4) inhibitor used to treat type 2 diabetes and is primarily metabolized by the liver enzyme CYP3A4. Uremia may affect the function of CYP3A4, which may have significant implications for the metabolism and elimination of evogliptin. By conducting population pharmacokinetics (PK) and pharmacodynamics (PD) modeling on evogliptin in patients with renal impairment, it is possible to predict the PK of drugs that are mainly metabolized by CYP3A4 in renal impairment conditions. This study aimed to construct a population PK and PD model of evogliptin in patients with varying degrees of kidney disease. Methods: This study implemented data from two clinical studies of evogliptin: an open-label, parallel-group clinical study conducted in patients with varying degrees of renal impairment and normal renal function (NCT02214693) and a single-dose, open-label, parallel-group study conducted in patients with end-stage renal disease (ESRD) and normal renal function (NCT04195919). In both studies, subjects were administered 5 mg evogliptin in a fasting state. A total of 46 subjects with 688 evogliptin concentration measurements and 598 DPP-4 activity measurements were available for analysis. PK/PD data for evogliptin, as well as potential covariate information including hematology, blood chemistry, and demographic data, were used to construct a population PK/PD model. The model construction used nonlinear mixed-effects modeling software (NONMEM® version 7.4) with first-order conditional estimation with interaction (FOCE-I). Each parameter was added to the structural model in a stepwise approach with forward and backward elimination, employing significance levels of 0.01 and 0.001, respectively. Nonparametric bootstrap resampling was used to evaluate model stability and to estimate confidence intervals (CIs) for the model parameters by repeatedly fitting the final model to bootstrap replicates (n = 1000) of the dataset. Prediction-corrected visual predictive checks (pcVPCs; 500 simulation replicates) were conducted to validate the final model. The final PK model was used to simulate concentration-time profiles, and the area under the concentration–time curve (AUC) from time zero to 120 h was derived, and the maximum plasma concentration (Cmax) was calculated, assuming a single dose of 5 mg in various covariate conditions. Results: A total of 46 participants with varying degrees of renal impairment and healthy subjects were enrolled. All subject groups had comparable demographic characteristics but different levels of renal impairment. A nonlinear mixed-effects model was developed to describe the population PK of evogliptin using 688 plasma PK samples. A two-compartment model with first-order absorption was selected as the base PK model on the basis of the Akaike information criterion (AIC), diagnostic plots, and objective function values (OFVs). The base PK model demonstrated reliable parameter estimation and a strong agreement between observed and predicted data without systematic bias. The significant covariates retained in the final model included chloride and amylase on Fr (relative bioavailability), age on CL/F (apparent clearance) and body weight on V3/F (peripheral volume of distribution). Varying chloride and amylase levels contributed to increasing the bioavailability of evogliptin. Lower clearance was observed in older patients, and body weight correlated with increasing V3/F. The goodness-of-fit plots indicated an adequate model structure for predicting evogliptin concentrations. The pcVPC showed an overlap between simulated and observed evogliptin concentrations, and bootstrapping resulted in 93.1% successful replication among 1000 replicates. The potential effects of relevant covariates on CYP3A4-mediated evogliptin PK were evaluated using Monte Carlo simulation. The simulation findings, in conjunction with previously reported PK data of evogliptin, provided evidence of a significant inhibition of first-pass metabolism in severe renal impairment conditions. A direct-link sigmoidal Emax model was developed to describe the relationship between plasma evogliptin concentration and DPP-4 inhibition. The final model robustly estimated PD parameters. The PK/PD model of evogliptin predicted near complete inhibition of DPP-4 at the maximum effect (Emax: 95.7%) and exhibited a low EC50 value (0.837 μg/L), suggesting the high potency and efficacy of evogliptin. Conclusion: The developed PK/PD model of evogliptin accurately predicted absorption, systemic exposure, and elimination variability in individuals with renal impairment. This study indicates that renal impairment and the resulting biochemical changes may impact the relative bioavailability of CYP3A4-metabolized drugs. This model serves as a basis for future evaluations of uremia's effect on nonrenal drug clearance and aids in optimizing dosing regimens for patients with renal impairment.ABSTRACT i Table of Contents vi List of Tables viii List of Figures ix List of Abbreviations xi Chapter 1. Introduction １ 1.1. Study Background １ 1.2. Purpose of research ４ Chapter 2. Methods 6 2.1. Clinical Study and Data Collection 6 2.1.1. Study Design and Population 6 2.1.2. PK Sample Collection and Bioanalytical Assay 10 2.1.3. PD Sample Collection and Bioanalytical Assay 13 2.2. Development of the Population PK Model 17 2.2.1. Base PK model development 17 2.2.2. Covariate PK model 18 2.2.3. Model validation 22 2.2.4. PK simulation in renal impairment patients 22 2.2.5. PD model of evogliptin 23 Chapter 3. Results 25 3.1. Clinical Study and Data Collection 25 3.1.1. Clinical study results 25 3.1.2. Pharmacokinetic results 28 3.1.3. Pharmacodynamic results 30 3.2. Development of the Population PK Model 32 2.2.1. Base model 32 2.2.2. Covariate model 37 2.2.3. Model validation 41 2.2.4. PK simulation 45 2.2.5. PD model of evogliptin 50 Chapter 4. Discussion 55 Chapter 5. Conclusion 65 Bibliography 66 Abstract in Korean 69박

Amelioration of Mitochondrial Bioenergetic Dysfunction in Diabetes Mellitus: Delving into Specialized and Non-specific Therapeutics for the Ailing Heart

Morbidity and mortality of the diabetic population is influenced by many confounding factors, but cardiovascular disease (CVD), remains the leading cause of death. Mitochondrial dysfunction is central in the development of cardiac contractile dysfunction, with decreased mitochondrial bioenergetic function, increased dependence on free fatty acid utilization, and a decrease in glucose utilization having been shown to contribute to contractile dysfunction. Strategies targeting the amelioration of mitochondrial bioenergetic function are attractive for limiting diabetes-induced heart failure, and preserving health-span. The goals of this dissertation were to assess two mitochondrial-centric approaches for the amelioration of mitochondrial and cardiac contractile dysfunction in diabetes mellitus. Our laboratory previously identified microRNA-378a (miR-378a) as a regulator of mitochondrially encoded ATP synthase membrane subunit 6 (mt-ATP6) mRNA, a component of the ATP synthase F0 complex. More recently, a second class of non-coding RNAs, long non-coding RNAs (lncRNA), have been proposed to regulate microRNA activity. LncRNA potassium voltage-gated channel subfamily Q member 1 overlapping transcript 1 (Kcnq1ot1), is predicted to bind miR-378a. Chapter 2 aimed to determine if inhibition of miR-378a could ameliorate cardiac contractile dysfunction in type 2 diabetes mellitus (T2DM), and to ascertain whether Kcnq1ot1 interacts with miR-378a to impact ATP synthase functionality by preserving mt-ATP6 levels. MiR-378a genomic loss, and inhibition by Kcnq1ot1, improved ATP synthase functionality, and preserved cardiac contractile function. Together, Kcnq1ot1 and miR-378a may act as constituents in an axis that regulates mt-ATP6 content. By acting as therapeutic targets, their manipulation may provide benefit to ATP synthase functionality in the heart during T2DM. A second method of ameliorating mitochondrial dysfunction is mitochondrial transplantation. Current literature suggests that mitochondrial transplantation may be of benefit to the diabetic heart. Chapter 3 aimed to assess mitochondrial transplantation as a prophylactic method of treating mitochondrial dysfunction in the diabetic heart. Following mitochondrial transplantation in vivo using ultrasound-guided echocardiography, mitochondrial signal was detectable in at least 30% of the left ventricle myocardium, primarily within and near injection sites. Poor mitochondrial distribution indicated a need for a more focused injection strategy aimed at targeting a cardiac region or segment of interest. Speckle tracking echocardiography has been utilized to evaluate spatial and progressive alterations in the diabetic heart independently, but the spatial and temporal manifestation of cardiac dysfunction remain elusive. Therefore, the objectives of Chapter 4 were to elucidate if cardiac dysfunction associated with T2DM occurs spatially, and if patterns of regional or segmental dysfunction manifest in a temporal fashion. Non-invasive echocardiography datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. Machine learning was used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. Overall, the Septal region, and the AntSeptum segment, best represented cardiac dysfunction associated with the diabetic state at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. These results suggested that cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the diabetic heart. Further, the Septal region, and AntSeptum segment, may provide a locale of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM

A machine learning based exploration of COVID-19 mortality risk

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, noninvasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage

Prospective Studies of Cardiovascular Risk Factors and Mild Cognitive Impairment

The association of cardiovascular risk factors including hypertension, diabetes, cholesterol, kidney function, and arterial stiffness with cognitive impairment in older adults is a well-studied phenomenon. However, there is considerably less evidence relating cardiovascular health specifically to a diagnosis of Mild Cognitive Impairment (MCI). As a precursor state of dementia, MCI is characterized by a decline in cognitive function from previous level, but not to the degree that activities of daily living are impaired. Not everyone who is diagnosed with MCI will eventually transition to dementia, but the transition rates are much higher compared to the general population (5-15% per year compared to 1-2%). The primary aim of the current investigation is to examine the relationship between individual cardiovascular risk factors and 5-year incident MCI risk and to investigate whether these relationships are moderated by apolipoprotein E genotype (APOE). An additional primary aim was to investigate whether an aggregation count of cardiovascular risk factors (MSLS-CVRFS) and two common cardiovascular risk factor profiles (FRS and ASCVD risk score) were related to 5-year incident MCI risk. Following exclusions for dementia, the study sample included 625 (Average baseline age: 61.98, 61% female) participants from the 6th and 7th waves of the Maine-Syracuse Longitudinal Study (MSLS). MCI diagnosis was made by a team of three investigators applying established MCI diagnostic criteria, with 96 participants diagnosed with possible MCI. Multiple logistic regression analysis was used to examine the association between individual baseline cardiovascular risk factors (SBP, TC, HDL, LDL, TRIG, GFR, THCY, Diabetes, PWV) and MCI with adjustment for basic demographic covariates including age, sex, years of education, and ethnicity. The same method was used for determining APOE interaction effects and relating cardiovascular risk factor scores (CVRFS, FRS, ASCVD) with MCI risk. Among individual risk factors, higher GFR and HDL were associated with lower MCI risk, while diabetes was associated with higher MCI risk. No APOE interaction effects were observed. All three of the cardiovascular risk factor scores tested were associated with higher MCI risk. These findings have clinical implications with regard to predicting MCI risk with a combination of cardiovascular risk factors. While these factors have previously been related to continuously distributed cognitive performance measures, it is critical that their relationship to a clinically defined binary outcome like MCI be investigated because treatment decisions are based on diagnosis

C-Reactive Protein Polymorphism and Serum Levels as an Independent Risk Factor in Sickle Cell Disease

This study explored the relationship of a dinucleotide repeat polymorphism in the intron of the CRP gene and serum CRP levels as independent risk factors for end-organ dysfunction (mild vs. severe) in adults with sickle cell disease. The pathogenesis of secondary complications of sickle cell disease is complex and poorly understood. Predicting the severity of these complications could assist in therapeutic decision-making. The study measured serum CRP levels and the number of CA intron repeats located on the CRP gene in 29 adults (31.74 ± 11.54 years) with sickle cell disease The hemoglobin genotypes were distributed as Hgb SS 48.6% (17 of n = 29), Hgb SC 20.0% (7 of n = 29), Sβ° 10.3% (3 of n = 29), and Sβ+ 6.9% (2 of n = 29). The sample was categorized as mild (n = 9) no end-organ dysfunction vs. severe (n = 21) documented end-organ dysfunction. The severe group was sub-categorized by specific organ dysfunctions, 9 with pulmonary hypertension, 6 with renal dysfunction and 6 with cerebral vascular accident. Examination of serum CRP levels found no significant association with severe end-stage organ dysfunction. There was no significant association between serum CRP level and the polymorphism. However, a significant negative correlation (rho = -0.401, p = 0.031) was found between glomerular filtration rates and CAhigh repeats (≥17). Previous studies have found an association of genetic variations in the CRP gene polymorphism to serum CRP levels. While this pilot study found no evidence of this association, the findings provide some rationale for further investigation of the repeat polymorphism in the CRP gene and its association with renal end-organ dysfunction