Diabetes and biomarkers

Doubly robust evaluation of high-dimensional surrogate markers

When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.

Impute the missing data using retrieved dropouts

Background

In the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of primary results. Some of the methods are based on the assumption of missing at random (MAR) which assumes subjects who discontinue the treatment will maintain the treatment effect after discontinuation. The agency, however, has expressed concern over methods based on this overly optimistic assumption, because it hardly holds for subjects discontinuing the investigational drug. Although in recent years a good number of sensitivity analyses based on missing not at random (MNAR) assumptions have been proposed, some use very conservative assumption on which it might be hard for sponsors and regulators to reach common ground.

Methods

Here we propose a multiple imputation method targeting at “treatment policy” estimand based on the MNAR assumption. This method can be used as the primary analysis, in addition to serving as a sensitivity analysis. It imputes missing data using information from retrieved dropouts defined as subjects who remain in the study despite occurrence of intercurrent events. Then imputed data long with completers and retrieved dropouts are analyzed altogether and finally multiple results are summarized into a single estimate. According to definition in ICH E9 (R1), this proposed approach fully aligns with the treatment policy estimand but its assumption is much more realistic and reasonable.

Results

Our approach has well controlled type I error rate with no loss of power. As expected, the effect size estimates take into account any dilution effect contributed by retrieved dropouts, conforming to the MNAR assumption.

Conclusions

Although multiple imputation approaches are always used as sensitivity analyses, this multiple imputation approach can be used as primary analysis for trials with sufficient retrieved dropouts or trials designed to collect retrieved dropouts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01509-9.

Estimation of the proportion of treatment effect explained by a high-dimensional surrogate

Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long-term follow-up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow-up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high-dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high-dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey-based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets.

Secondary Metabolites in the Treatment of Diabetes Mellitus: A Paradigm Shift

Diabetes mellitus (DM) is a chronic, polygenic and non-infectious group of diseases that occurs due to insulin resistance or its low production by the pancreas and is also associated with lifelong damage, dysfunction and collapse of various organs. Management of diabetes is quite complex having many bodily and emotional complications and warrants efficient measures for prevention and control of the same. As per the estimates of the current and future diabetes prevalence, around 425 million people were diabetic in 2017 which is anticipated to rise up to 629 million by 2045. Various studies have vaguely proven the fact that several vitamins, minerals, botanicals and secondary metabolites demonstrate hypoglycemic activity in vivo as well as in vitro. Flavonoids, anthocyanin, catechin, lipoic acid, coumarin metabolites, etc. derived from herbs were found to elicit a significant influence on diabetes. However, the prescription of herbal compounds depend on various factors, including the degree of diabetes progression, comorbidities, feasibility, economics as well as their ADR profile. For instance, cinnamon could be a more favorable choice for diabetic hypertensive patients. Diabecon®, Glyoherb® and Diabeta Plus® are some of the herbal products that had been launched in the market for the favorable or adjuvant therapy of diabetes. Moreover, Aloe vera leaf gel extract demonstrates significant activity in diabetes. The goal of this review was to inscribe various classes of secondary metabolites, in particular those obtained from plants, and their role in the treatment of DM. Recent advancements in recognizing the markers which can be employed for identifying altered metabolic pathways, biomarker discovery, limitations, metabolic markers of drug potency and off-label effects are also reviewed.

Using a surrogate marker for early testing of a treatment effect

The development of methods to identify, validate, and use surrogate markers to test for a treatment effect has been an area of intense research interest given the potential for valid surrogate markers to reduce the required costs and follow-up times of future studies. Several quantities and procedures have been proposed to assess the utility of a surrogate marker. However, few methods have been proposed to address how one might use the surrogate marker information to test for a treatment effect at an earlier time point, especially in settings where the primary outcome and the surrogate marker are subject to censoring. In this paper, we propose a novel test statistic to test for a treatment effect using surrogate marker information measured prior to the end of the study in a time-to-event outcome setting. We propose a robust nonparametric estimation procedure and propose inference procedures. In addition, we evaluate the power for the design of a future study based on surrogate marker information. We illustrate the proposed procedure and relative power of the proposed test compared to a test performed at the end of the study using simulation studies and an application to data from the Diabetes Prevention Program.

Receiver Operating Characteristic Analysis and Clinical Trial Simulation to Inform Dose Titration Decisions

Optimal dose selection in clinical trials is problematic when efficacious and toxic concentrations are close. A novel quantitative approach follows for optimizing dose titration in clinical trials. A system of pharmacokinetics (PK), pharmacodynamics, efficacy, and toxicity was simulated for scenarios characterized by varying degrees of different types of variability. Receiver operating characteristic (ROC) and clinical trial simulation (CTS) were used to optimize drug titration by maximizing efficacy/safety. The scenarios included were a low-variability base scenario, and high residual (20%), interoccasion (20%), interindividual (40%), and residual plus interindividual variability scenarios, and finally a shallow toxicity slope scenario. The percentage of subjects having toxicity was reduced by 87.4% to 93.5%, and those having efficacy was increased by 52.7% to 243%. Interindividual PK variability may have less impact on optimal cutoff values than other sources of variability. ROC/CTS methods for optimizing dose titration offer an individualized approach that leverages exposure-response relationships.

The relationship of serum endocan levels and anti-TNF-alpha therapy in patients with ankylosing spondylitis

OBJECTIVE

Endocan is a marker for vascular pathogenesis and important mediator of angiogenesis that strongly associates with inflammation and vascular diseases. Growing evidence suggest that inflammatory cytokine tumor necrosis factor (TNF-alpha) plays a role in its regulation and secretion, whereas TNF-alpha inhibitors may have the opposite influence. The aim of this research is to investigate the association between serum endocan and anti-TNF-alpha drug treatment in patients with ankylosing spondylitis (AS).

METHODS

Serum endocan levels were analyzed in 42 patients with AS under anti-TNF-alpha usage. Control group consisted of 37 patients with AS who are not receiving anti-TNF drugs. Endocan is analyzed using ESM-1 ELISA kits. The blood glucose and lipid measurements of patients were also assessed.

RESULTS

There was no significant change in serum endocan levels among groups. The total cholesterol, triglyceride, and LDL-C levels were higher in patients receiving anti-TNF-alpha; however, differences were not significant. There was no significant correlation between serum endocan levels and blood lipid measurements.

CONCLUSION

Anti-TNF-alpha treatment does not affect serum endocan levels in patients with AS. This research has been first to evaluate the relationship between serum endocan and anti-TNF-alpha therapy in AS. Future studies are necessary to verify the exact role of anti-TNF-alpha therapy on serum endocan levels in patients with AS.

The effect of health on socioeconomic status: Using instrumental variables to revisit a successful randomized controlled trial

Poor health is widely recognized as a consequence of social disadvantage, but health problems may also help transmit social disadvantage over time and generations. Experimentally assigned health exposures, namely those tested in randomized controlled trials, may provide opportunities to estimate the causal effects of health on socioeconomic status (SES). We revisit data from the Diabetes Control and Complications Trial, a randomized controlled trial of glucose control therapy in Type 1 diabetic patients, and use treatment assignment as an instrument for health status to test the causal effect of treatment-related health improvement on subsequent SES measured during the trial's follow-up study, the Epidemiology of Diabetes Interventions and Complications study. We used the Two-Factor Hollingshead Index of Social Position, which comprises education and occupation, to measure SES. Glycated hemoglobin (HbA1c) served as a proxy for health status. Ordinary least squares (OLS) regression models showed that lower HbA1c at the trial's end was associated with higher SES at both the start of the follow-up and 17 years later. However, instrumental variable analyses showed no causal effect of HbA1c on SES, suggesting that OLS estimates are biased by endogeneity. Sensitivity analyses showed robustness to several alternate specifications. While the HbA1c advantage conferred by random assignment to treatment within the trial did not produce higher Hollingshead Index scores, we note that occupation and education categories may be harder to affect than are outcomes such as income. This analysis offers evidence that clinical trial data may be a rich and unrecognized resource for estimating health effects on SES.

Recent advances in molecular biomarkers for diabetes mellitus: a systematic review

CONTEXT

Diabetes is a growing global metabolic epidemic. Current research is focussing on exploring how the biological processes and clinical outcomes of diabetes are related and developing novel biomarkers to measure these relationships, as this can subsequently improve diagnostic, therapeutic and management capacity.

OBJECTIVE

The objective of this study is to identify the most recent advances in molecular biomarkers of diabetes and directions that warrant further research.

METHODS

Using a systematic search strategy, the MEDLINE, CINAHL and OVID MEDLINE databases were canvassed for articles that investigated molecular biomarkers for diabetes. Initial selections were made based on article title, whilst final inclusion was informed by a critical appraisal of the full text of each article.

RESULTS

The systematic search returned 246 records, of which 113 were unique. Following screening, 29 records were included in the final review. Three main research strategies (the development of novel technologies, broad biomarker panels, and targeted approaches) identified a number of potential biomarkers for diabetes including miR-126, C-reactive protein, 2-aminoadipic acid and betatrophin.

CONCLUSION

The most promising research avenue identified is the detection and quantification of micro RNA. Further, the utilisation of functionalised electrodes as a means to detect biomarker compounds also warrants attention.

Machine Learning and Data Mining Methods in Diabetes Research

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Precision Medicine, Diabetes, and the U.S. Food and Drug Administration

The U.S. Food and Drug Administration (FDA) has long sought to achieve the broader use of personalized medicine, which is better targeting of FDA-approved therapies through incorporating precise knowledge of a patient's underlying condition to therapies optimally chosen to match those needs. While some strides have been made in precision medicine-particularly in oncology and rare genetic diseases-most of the standard general medicine indications have yet to realize the benefits of precision-guided therapies. This includes those for diabetes mellitus (DM), both type 1 and type 2. Although the scientific and regulatory considerations needed to move to a more "precise" future of DM prevention and treatment differ between the two disease subsets, scientific advances in both must occur before the FDA can incorporate precision medicine into its oversight of DM drug development and approval. This article provides an overview of the regulatory expectations and challenges in realizing a future where the therapeutics for DM are informed by precise knowledge of a patient's genetics and specific phenotype.

Exploratory Metabolomics Profiling in the Kainic Acid Rat Model Reveals Depletion of 25-Hydroxyvitamin D3 during Epileptogenesis

Currently, no reliable markers are available to evaluate the epileptogenic potential of a brain injury. The electroencephalogram is the standard method of diagnosis of epilepsy; however, it is not used to predict the risk of developing epilepsy. Biomarkers that indicate an individual's risk to develop epilepsy, especially those measurable in the periphery are urgently needed. Temporal lobe epilepsy (TLE), the most common form of acquired epilepsy, is characterized by spontaneous recurrent seizures following brain injury and a seizure-free "latent" period. Elucidation of mechanisms at play during epilepsy development (epileptogenesis) in animal models of TLE could enable the identification of predictive biomarkers. Our pilot study using liquid chromatography-mass spectrometry metabolomics analysis revealed changes (p-value ≤ 0.05, ≥1.5-fold change) in lipid, purine, and sterol metabolism in rat plasma and hippocampus during epileptogenesis and chronic epilepsy in the kainic acid model of TLE. Notably, disease development was associated with dysregulation of vitamin D3 metabolism at all stages and plasma 25-hydroxyvitamin D3 depletion in the acute and latent phase of injury-induced epileptogenesis. These data suggest that plasma VD3 metabolites reflect the severity of an epileptogenic insult and that a panel of plasma VD3 metabolites may be able to serve as a marker of epileptogenesis.

Cardiovascular outcome trials for anti-diabetes medication: A holy grail of drug development?

Since the time questions arose on cardiovascular safety of Rosiglitazone, FDA has suggested guidelines on conduct of studies on anti-diabetic drugs so as to prove that the cardiovascular risk is acceptable. Based on the cardiovascular risks of pre-approval clinical trials, guidelines have been made to conduct cardiovascular safety outcome trials (CVSOTs) prior to the drug approval or after the drug has been approved. Unlike the trials comparing the efficacy of antidiabetic agents, the CVSOTs examine the cardiovascular safety of a drug in comparison to standard of care. These trials are expensive aspects of drug development and are associated with various technical and operational challenges. More cost effective models of assessing cardiovascular safety like use of biomarkers, electronic medical records, pragmatic and factorial designs can be adopted. This article critically looks at the antidiabetic drug approval from a cardiovascular perspective by asking a few questions and arriving at answers.

Utility of different glycemic control metrics for optimizing management of diabetes

The benchmark for assessing quality of long-term glycemic control and adjustment of therapy is currently glycated hemoglobin (HbA1c). Despite its importance as an indicator for the development of diabetic complications, recent studies have revealed that this metric has some limitations; it conveys a rather complex message, which has to be taken into consideration for diabetes screening and treatment. On the basis of recent clinical trials, the relationship between HbA1c and cardiovascular outcomes in long-standing diabetes has been called into question. It becomes obvious that other surrogate and biomarkers are needed to better predict cardiovascular diabetes complications and assess efficiency of therapy. Glycated albumin, fructosamin, and 1,5-anhydroglucitol have received growing interest as alternative markers of glycemic control. In addition to measures of hyperglycemia, advanced glucose monitoring methods became available. An indispensible adjunct to HbA1c in routine diabetes care is self-monitoring of blood glucose. This monitoring method is now widely used, as it provides immediate feedback to patients on short-term changes, involving fasting, preprandial, and postprandial glucose levels. Beyond the traditional metrics, glycemic variability has been identified as a predictor of hypoglycemia, and it might also be implicated in the pathogenesis of vascular diabetes complications. Assessment of glycemic variability is thus important, but exact quantification requires frequently sampled glucose measurements. In order to optimize diabetes treatment, there is a need for both key metrics of glycemic control on a day-to-day basis and for more advanced, user-friendly monitoring methods. In addition to traditional discontinuous glucose testing, continuous glucose sensing has become a useful tool to reveal insufficient glycemic management. This new technology is particularly effective in patients with complicated diabetes and provides the opportunity to characterize glucose dynamics. Several continuous glucose monitoring (CGM) systems, which have shown usefulness in clinical practice, are presently on the market. They can broadly be divided into systems providing retrospective or real-time information on glucose patterns. The widespread clinical application of CGM is still hampered by the lack of generally accepted measures for assessment of glucose profiles and standardized reporting of glucose data. In this article, we will discuss advantages and limitations of various metrics for glycemic control as well as possibilities for evaluation of glucose data with the special focus on glycemic variability and application of CGM to improve individual diabetes management.

The contextual influence of coal abandoned mine lands in communities and type 2 diabetes in Pennsylvania

Coal abandoned mine lands (AMLs), persistent and prevalent across Pennsylvania, offer an instructive evaluation of potential contextual influences of chronic environmental contamination (CEC) on individual health. We evaluated associations between the burden of AMLs, represented by 10 contextual metrics at the community level, and individual-level type 2 diabetes using hemoglobin A1c (HbA1c) as a biomarker. Cross-sectional and longitudinal multilevel analyses were conducted with over 28,000 diabetic primary care patients of the Geisinger Clinic. Adjusted models revealed five AML burden measures were associated (p<0.05), and three additional were borderline associated (0.05≤p≤0.10), with higher and/or change in HbA1c levels. This study provides key empirical evidence of adverse impacts of CEC in communities on an important chronic disease, illustrating the contextual effects of living in long-term degraded landscapes and communities.

Association of intercellular adhesion molecule 1 (ICAM1) with diabetes and diabetic nephropathy

Diabetes and diabetic nephropathy are complex diseases affected by genetic and environmental factors. Identification of the susceptibility genes and investigation of their roles may provide useful information for better understanding of the pathogenesis and for developing novel therapeutic approaches. Intercellular adhesion molecule 1 (ICAM1) is a cell surface glycoprotein expressed on endothelial cells and leukocytes in the immune system. The ICAM1 gene is located on chromosome 19p13 within the linkage region of diabetes. In the recent years, accumulating reports have implicated that genetic polymorphisms in the ICAM1 gene are associated with diabetes and diabetic nephropathy. Serum ICAM1 levels in diabetes patients and the icam1 gene expression in kidney tissues of diabetic animals are increased compared to the controls. Therefore, ICAM1 may play a role in the development of diabetes and diabetic nephropathy. In this review, we present genomic structure, variation, and regulation of the ICAM1 gene, summarized genetic and biological studies of this gene in diabetes and diabetic nephropathy and discussed about the potential application using ICAM1 as a biomarker and target for prediction and treatment of diabetes and diabetic nephropathy.