Heterogeneity Of Disease Progression Research Articles

Type 2 diabetes pathway-specific polygenic risk scores elucidate heterogeneity in clinical presentation, disease progression and diabetic complications in 18,217 Chinese individuals with type 2 diabetes.

Type 2 diabetes is a complex and heterogeneous disease and the aetiological components underlying the heterogeneity remain unclear in the Chinese and East Asian population. Therefore, we aimed to investigate whether specific pathophysiological pathways drive the clinical heterogeneity in type 2 diabetes. We employed newly developed type 2 diabetes hard-clustering and soft-clustering pathway-specific polygenic risk scores (psPRSs) to characterise individual genetic susceptibility to pathophysiological pathways implicated in type 2 diabetes in 18,217 Chinese patients from Hong Kong. The 'total' type 2 diabetes polygenic risk score (PRS) was summed by genome-wide significant type 2 diabetes signals (n=1289). We examined the associations between psPRSs and cardiometabolic profile, age of onset, two glycaemic deterioration outcomes (clinical requirement of insulin treatment, defined by two consecutive HbA1c values ≥69 mmol/mol [8.5%] more than 3 months apart during treatment with two or more oral glucose-lowering drugs, and insulin initiation), three renal (albuminuria, end-stage renal disease and chronic kidney disease) outcomes and five cardiovascular outcomes. Although most psPRSs and total type 2 diabetes PRS were associated with an earlier and younger onset of type 2 diabetes, the psPRSs showed distinct associations with clinical outcomes. In particular, individuals with normal weight showed higher psPRSs for beta cell dysfunction and lipodystrophy than those who were overweight. The psPRSs for obesity were associated with faster progression to clinical requirement of insulin treatment (adjusted HR [95% CI] 1.09 [1.05, 1.13], p<0.0001), end-stage renal disease (1.10 [1.04, 1.16], p=0.0007) and CVD (1.10 [1.05, 1.16], p<0.0001) while the psPRSs for beta cell dysfunction were associated with reduced incident end-stage renal disease (0.90 [0.85, 0.95], p=0.0001) and heart failure (0.83 [0.73, 0.93], p=0.0011). Major findings remained significant after adjusting for a set of clinical variables. Beta cell dysfunction and lipodystrophy could be the driving pathological pathways in type 2 diabetes in individuals with normal weight. Genetic risks of beta cell dysfunction and obesity represent two major genetic drivers of type 2 diabetes heterogeneity in disease progression and diabetic complications, which are shared across ancestry groups. Type 2 diabetes psPRSs may help inform patient stratification according to aetiology and guide precision diabetes care.

Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study

BackgroundAmyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data.MethodsThree classification problems were considered: predicting death (binary problem), predicting death or percutaneous endoscopic gastrostomy (PEG) (multiclass problem), and predicting death or non-invasive ventilation (NIV) (multiclass problem). Two supervised learning models, a logistic regression (LR) and a deep learning multilayer perceptron (MLP), were trained ensuring technical robustness and reproducibility. Moreover, to provide insights into model explainability and result interpretability, model coefficients for LR and Shapley values for both LR and MLP were considered to characterize the relationship between each variable and the outcome.ResultsOn the one hand, predicting death was successful as both models yielded F1 scores and accuracy well above 0.7. The model explainability analysis performed for this outcome allowed for the understanding of how different methodological approaches consider the input variables when performing the prediction. On the other hand, predicting death alongside PEG or NIV proved to be much more challenging (F1 scores and accuracy in the 0.4-0.6 interval).ConclusionsIn conclusion, predicting death due to ALS proved to be feasible. However, predicting PEG or NIV in a multiclass fashion proved to be unfeasible with these data, regardless of the complexity of the methodological approach. The observed results suggest a potential ceiling on the amount of information extractable from the database, e.g., due to the intrinsic difficulty of the prediction tasks at hand, or to the absence of crucial predictors that are, however, not currently collected during routine practice.

Profiles of disease progression and predictors of mortality in Colombian patients with amyotrophic lateral sclerosis: a comprehensive longitudinal study

Objective: This study aimed to assess the prognostic value of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) in predicting mortality and characterizing disease progression patterns in ALS patients in Colombia. Methods: We conducted a retrospective longitudinal analysis of 537 ALS patients from the Roosevelt Institute Rehabilitation Service between October 2008 and October 2022. The study excluded nine patients due to incomplete data, resulting in 528 individuals in the analysis. ALS diagnoses were confirmed using the revised El Escorial and Gold Coast criteria. Disease progression was assessed using the ALSFRS-R, and mortality data were sourced from follow-up calls and a national database. Statistical analysis included Cox proportional hazards models to identify mortality predictors and Growth Mixture Modeling (GMM) to explore ALS progression trajectories. Results: The majority of the cohort (63.8%) deceased within the 84-month follow-up period. Survival analysis revealed that each point increase in the ALSFRS-R rate was associated with a 2.22-fold (95% CI =1.99–2.48, p < 0.001) increased risk of mortality. In the population with data from two clinical visits, the ALSFRS-R rate based on initial assessments predicted mortality more effectively over 36 months than the rate based on two evaluations. GMM identified three distinct progression trajectories: slow, intermediate, and rapid decliners. Conclusions: The ALSFRS-R rate, derived from self-reported symptom onset, significantly predicts mortality, underscoring its value in clinical assessments. This study highlights the heterogeneity in disease progression among Colombian ALS patients, indicating the necessity for personalized treatment approaches based on individual progression trajectories. Further studies are needed to refine these predictive models and improve patient management and outcomes.

Distinct clinical trajectories of gastrointestinal progression among patients with systemic sclerosis.

Systemic sclerosis (SSc) is heterogeneous in its clinical presentation. Common manifestations cluster together, defining unique subgroups. This investigation aims to characterize gastrointestinal (GI) phenotypes and determine whether they can be distinguished by temporal progression. We examine a well-established SSc patient cohort with a modified Medsger GI severity score measured over time to determine heterogeneity in disease progression. Growth mixture models estimate each patient's phenotype and disease severity trajectory over time. We compare the characteristics of estimated phenotypes using non-parametric statistics and linear and logistic regression to compare patient characteristics between phenotypes while adjusting for disease duration. We examined 2696 SSc patients with at least two Medsger GI scores, identifying four unique phenotypes. The most common phenotype (n = 2325) ("Stable") had an average score of 1 that was consistent over time. Two phenotypes were progressive ["Early Progressive" (n = 142) and "Late Progressive" (n = 115)] with an initial average score of 1. The Early Progressive group increased initially and stabilized, and the Late Progressive group worsened slowly over time. A fourth phenotype ["Early Severe GI"; (n = 114)] had an initial average Medsger GI score just below 3 with high mortality and improving GI severity over time. Clinically distinct GI phenotypes exist among patients with SSc. These phenotypes are not only distinguished by GI and extra-intestinal SSc clinical complications, but they are also temporally distinct. Distinct autoantibody profiles are associated strongly with more severe GI disease.

Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns

Identifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups.

Identifying chronic kidney disease stage 3 with excess disease burden.

Chronic kidney disease (CKD) is a widely prevalent disease with heterogeneous disease progression. Prior study findings suggest that early referral to nephrologists can improve health outcomes for patients with CKD. Current practice guidelines recommend nephrology referral when patients are diagnosed with CKD stage 4. We tested whether a subset of patients with CKD stage 3 and common medical comorbidities demonstrates disease progression, cost, and utilization patterns that would merit earlier referral. Retrospective study of Medicare fee-for-service beneficiaries with CKD stages 3 through 5 and end-stage kidney disease. We identified 7 comorbidities with high prevalence in patients with progressive CKD and segmented beneficiaries with CKD stage 3 based on the presence of these comorbidities. Outcomes including costs, utilization, and disease progression were then compared across beneficiaries with different stages of CKD. We identified that beneficiaries with CKD stage 3 and at least 1 of the selected comorbidities (CKD stage 3-plus) represented 35.4% of all beneficiaries with CKD stage 3. The CKD stage 3-plus cohort had cost and utilization patterns that were more similar to beneficiaries with CKD stages 4 and 5 than to beneficiaries with CKD stage 3 without the selected comorbidities. Our findings demonstrate the use of a claims-based algorithm to identify patients with CKD stage 3 who have high costs and are at risk of disease progression, highlighting a potential subset of patients who might benefit from earlier nephrology intervention.

Cohort profile: the ‘Biomarkers of heterogeneity in type 1 diabetes’ study—a national prospective cohort study of clinical and metabolic phenotyping of individuals with long-standing type 1 diabetes in the Netherlands

PurposeThe ‘Biomarkers of heterogeneity in type 1 diabetes’ study cohort was set up to identify genetic, physiological and psychosocial factors explaining the observed heterogeneity in disease progression and the development...

Definition and analysis of gray matter atrophy subtypes in mild cognitive impairment based on data-driven methods.

Mild cognitive impairment (MCI) is an important stage in Alzheimer's disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype. In this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed. Three MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE ε4 is an important factor that cause the conversion of MCI to AD. It was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI.

Ultrasonographic Insights into Peripheral Psoriatic Arthritis: Updates in Diagnosis and Monitoring.

Psoriatic arthritis (PsA) is a chronic inflammatory arthritis associated with psoriasis, characterized by heterogeneous clinical manifestations and variable disease progression. Ultrasonography has emerged as a valuable tool in the diagnosis and monitoring of PsA, providing real-time visualization of joint and soft tissue abnormalities. This review highlights recent advancements in ultrasonographic techniques for the assessment of PsA, including the identification of typical features, the role of power Doppler imaging in detecting active inflammation, and the potential of ultrasound for guiding treatment decisions. Additionally, we discuss the utility of ultrasound in assessing treatment response and monitoring disease progression in patients with PsA, with a focus on novel imaging modalities. By elucidating the evolving role of ultrasonography in PsA management, this article aims to enhance clinicians' understanding of its utility in facilitating early diagnosis, optimizing treatment strategies, and improving patient outcomes.

Acute Kidney Injury and Subsequent Kidney Failure With Replacement Therapy Incidence in Older Adults With Advanced CKD: A Cohort Study of US Veterans

Advanced age is a major risk factor for chronic kidney disease (CKD) development, which has high heterogeneity in disease progression. Acute kidney injury (AKI) hospitalization rates are increasing, especially among older adults. Previous AKI epidemiologic analyses have focused on hospitalized populations, which may bias results toward sicker populations. This study examined the association between AKI and incident kidney failure with replacement therapy (KFRT) while evaluating age as an effect modifier of this relationship. Retrospective cohort study. 24,133 Veterans at least 65 years old with incident CKD stage 4 from 2011 to2013. AKI, AKI severity, and age. KFRT and death. The Fine-Gray competing risk regression was used to model AKI and incident KFRT with death as a competing risk. A Cox regression was used to model AKI severity and death. Despite a nonsignificant age interaction between AKI and KFRT, a clinically relevant combined effect of AKI and age on incident KFRT was observed. Compared with our oldest age group without AKI, those aged 65-74 years with AKI had the highest risk of KFRT (subdistribution HR [sHR], 14.9; 95% CI, 12.7-17.4), whereas those at least 85 years old with AKI had the lowest (sHR, 1.71; 95% CI, 1.22-2.39). Once Veterans underwent KFRT, their risk of death increased by 44%. A 2-fold increased risk of KFRT was observed across all AKI severity stages. However, the risk of death increased with worsening AKI severity. Our study lacked generalizability, was restricted to ever use of medications, and used inpatient serum creatinine laboratory results to define AKI and AKI severity. In this national cohort, advanced age was protective against incident KFRT but not death. This is likely explained by the high frequency of deaths observed in this population (51.1%). Nonetheless, AKI and younger age are substantial risk factors for incident KFRT.

Genome-wide DNA methylation analysis related to ALS patient progression and survival.

Epigenetics contributes to the pathogenesis of amyotrophic lateral sclerosis (ALS). We aimed to characterize the DNA methylation profiles associated with clinical heterogeneity in disease progression and survival among patients. We included a cohort of 41 patients with sporadic ALS, with a median follow-up of 86.9months, and 27 rigorously matched healthy controls. Blood-based genome-wide DNA methylation analysis was conducted. A total of 948 progression rate-associated differentially methylated positions, 298 progression rate-associated differentially methylated regions (R-DMRs), 590 survival time-associated DMPs, and 197 survival time-associated DMRs (S-DMRs) were identified, using complementary grouping strategies. Enrichment analysis of differentially methylated genes highlighted the involvement of synapses and axons in ALS progression and survival. Clinical analysis revealed a positive correlation between the average methylation levels of the R-DMR in PRDM8 and disease progression rate (r = 0.479, p = 0.002). Conversely, there was an inverse correlation between the average methylation levels of the R-DMR in ANKRD33 and disease progression rate (r = -0.476, p = 0.002). In addition, patients with higher methylation levels within the S-DMR of ZNF696 experienced longer survival (p = 0.016), while those with elevated methylation levels in the S-DMR of RAI1 had shorter survival (p = 0.006). DNA methylation holds promise as a potential biomarker for tracking disease progression and predicting survival outcome and also offers targets for precision medicine.

A future of AI-driven personalized care for people with multiple sclerosis.

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records.

There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes. The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management. This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission. A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83. Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review

BackgroundType 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification.MethodsTo understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ge50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument.ResultsWe identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings.ConclusionsWhile the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design.

Risk Stratification in Primary Biliary Cholangitis.

Primary Biliary Cholangitis (PBC) is a chronic cholestatic liver disease with a heterogeneous presentation, symptomatology, disease progression, and response to therapy. The current risk stratification assessment, aimed at identifying patients with a higher risk of disease progression, encompasses an in-depth analysis of demographic data, clinical and laboratory findings, antibody profiles, and the evaluation of liver fibrosis using both invasive and noninvasive techniques. Treatment response scores after one year of therapy remain to date a major factor influencing the prognosis of PBC patients. While the initial therapeutic approach with ursodeoxycholic acid (UDCA) is universally applied, new second-line treatment options have recently emerged, with many others under investigation. Consequently, the prevailing one-size-fits-all approach is poised to be supplanted by tailored strategies, ensuring high-risk patients receive the most appropriate treatment regimen from diagnosis. This will require the development of a risk prediction model to assess, at the time of diagnosis, the course, outcome, and response to first and additional treatments of PBC patients. This manuscript provides a comprehensive overview of the current and emerging tools used for risk stratification in PBC and speculates on how these developments might shape the disease landscape in the near future.

Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time. A total of 1260 ALS patients from the PRO-ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score. Three baseline covariates and seven time-dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6-month survival probability according to the longitudinal information of individual patients. We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report.

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of invivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Targeting Skeletal Muscle Dysfunction With L-Carnitine for the Treatment of Patients With Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a major medical problem and the world's third leading cause of death. COPD is a chronic disease with heterogeneous clinical symptoms, disease progression, and treatment responses. Besides pulmonary symptomatology, the common systemic clinical manifestations are cachexia, muscle weakness, and widespread comorbidities such as cardiovascular diseases, diabetes, osteoporosis, and infections. The adverse effects of pharmaceutical therapies contribute to the difficulty of health risk assessment and management of COPD patients. This review shows how skeletal muscle dysfunction and metabolic abnormalities contribute significantly to COPD patients' symptoms, functional activities, quality of life, and overall disease outcomes. Based on the clinical evidence of L-carnitine and derivatives as metabolic and muscle bioenergetic enhancers, we propose broader research and implementation of this nutraceutical agent as an effective, inexpensive, and safe adjuvant therapeutic for the long-term management of COPD patients. Moreover, we believe the management of COPD as a chronic disease should be shifted from symptomatic reactive pharmaceutical intervention to more constructive and non-toxic approaches using a single or combination of natural and nutritional agents with potential muscle metabolic enhancing and immunomodulating activities to achieve a better overall outcome for the patients in terms of morbidity, mortality, and medical cost-reduction.

A Low-Cost Sequencing Platform for Rapid Genotyping in ADPKD and its Impact on Clinical Care

Autosomal-dominant polycystic kidney disease (ADPKD) is the most common genetic cause of kidney failure. Because of the heterogeneity in disease progression in ADPKD, parameters predicting future outcome are important. The disease-causing genetic variant is one of these parameters. A multiplex polymerase chain reaction (PCR)-based panel (MPP) was established for analysis of 6 polycystic kidney disease (PKD) genes (PKD1, PKD2, HNF1B, GANAB, DZIP1L, and PKHD1) in 441 patients with ADPKD. Selected patients were additionally sequenced using Sanger sequencing or a custom enrichment-based gene panel. Results were combined with clinical characteristics to assess the impact of genetic data on clinical decision-making. Variants of unclear significance (VUS) were considered diagnostic based on a classic ADPKD clinical phenotype. Using the MPP, disease-causing variants were detected in 65.3% of patients. Sanger sequencing and the custom gene panel in 32 patients who were MPP-negative revealed 20 variants missed by MPP, (estimated overall false negative rate 24.6%, false-positive rate 9.4%). Combining clinical and genetic data revealed that knowledge of the genotype could have impacted the treatment decision in 8.2% of patients with a molecular genetic diagnosis. Sequencing only the PKD1 pseudogene homologous region in MPP-negative patients resulted in an acceptable false-negative rate of 3.28%. The MPP yields rapid genotype information at lower costs and allows for simple extension of the panel for new disease genes. Additional sequencing of the PKD1 pseudogene homologous region is required in negative cases. Access to genotype information even in settings with limited resources is important to allow for optimal patient counseling in ADPKD.

FBP1-Altered Carbohydrate Metabolism Reduces Leukemic Viability through Activating P53 and Modulating the Mitochondrial Quality Control System In Vitro.

Acute myeloid leukemia (AML)—the most frequent form of adult blood cancer—is characterized by heterogeneous mechanisms and disease progression. Developing an effective therapeutic strategy that targets metabolic homeostasis and energy production in immature leukemic cells (blasts) is essential for overcoming relapse and improving the prognosis of AML patients with different subtypes. With respect to metabolic regulation, fructose-1,6-bisphosphatase 1 (FBP1) is a gluconeogenic enzyme that is vital to carbohydrate metabolism, since gluconeogenesis is the central pathway for the production of important metabolites and energy necessary to maintain normal cellular activities. Beyond its catalytic activity, FBP1 inhibits aerobic glycolysis—known as the “Warburg effect”—in cancer cells. Importantly, while downregulation of FBP1 is associated with carcinogenesis in major human organs, restoration of FBP1 in cancer cells promotes apoptosis and prevents disease progression in solid tumors. Recently, our large-scale sequencing analyses revealed FBP1 as a novel inducible therapeutic target among 17,757 vitamin-D-responsive genes in MV4-11 or MOLM-14 blasts in vitro, both of which were derived from AML patients with FLT3 mutations. To investigate FBP1′s anti-leukemic function in this study, we generated a new AML cell line through lentiviral overexpression of an FBP1 transgene in vitro (named FBP1-MV4-11). Results showed that FBP1-MV4-11 blasts are more prone to apoptosis than MV4-11 blasts. Mechanistically, FBP1-MV4-11 blasts have significantly increased gene and protein expression of P53, as confirmed by the P53 promoter assay in vitro. However, enhanced cell death and reduced proliferation of FBP1-MV4-11 blasts could be reversed by supplementation with post-glycolytic metabolites in vitro. Additionally, FBP1-MV4-11 blasts were found to have impaired mitochondrial homeostasis through reduced cytochrome c oxidase subunit 2 (COX2 or MT-CO2) and upregulated PTEN-induced kinase (PINK1) expressions. In summary, this is the first in vitro evidence that FBP1-altered carbohydrate metabolism and FBP1-activated P53 can initiate leukemic death by activating mitochondrial reprogramming in AML blasts, supporting the clinical potential of FBP1-based therapies for AML-like cancers.