Estimating Glycemia From HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy.

OBJECTIVE: To examine the accuracy of different periods of continuous glucose monitoring (CGM), hemoglobin A1c (HbA1c), and their combination for estimating mean glycemia over 90 days (AG90). RESEARCH DESIGN AND METHODS: We retrospectively studied 985 CGM periods of 90 days with <10% missing data from 315 adults (86% of whom had type 1 diabetes) with paired HbA1c measurements. The impact of mean red blood cell age as a proxy for nonglycemic effects on HbA1c was estimated using published theoretical models and in comparison with empirical data. Given the lack of a gold standard measurement for AG90, we applied correction methods to generate a reference (eAG90) that we used to assess accuracy for HbA1c and CGM. RESULTS: Using 14 days of CGM at the end of the 90-day period resulted in a mean absolute error (95th percentile) of 14 (34) mg/dL when compared with eAG90. Nonglycemic effects on HbA1c led to a mean absolute error for average glucose calculated from HbA1c of 12 (29) mg/dL. Combining 14 days of CGM with HbA1c reduced the error to 10 (26) mg/dL. Mismatches between CGM and HbA1c >40 mg/dL occurred more than 5% of the time. CONCLUSIONS: The accuracy of estimates of eAG90 from limited periods of CGM can be improved by averaging with an HbA1c-based estimate or extending the monitoring period beyond ∼26 days. Large mismatches between eAG90 estimated from CGM and HbA1c are not unusual and may persist due to stable nonglycemic factors.

The Effects of Daily Prednisone and Tocilizumab on Hemoglobin A1c During the Treatment of Giant Cell Arteritis.

OBJECTIVE: To study the longitudinal effects of both glucocorticoids and tocilizumab, an interleukin-6 receptor inhibitor, on hemoglobin A1c (HbA1c ) levels during glucocorticoid tapering. METHODS: We analyzed patients with complete data from the Giant Cell Arteritis Clinical Research Study (GiACTA) to investigate the impact of both glycemic and nonglycemic factors on changes in HbA1c levels over the 52-week trial. Giant cell arteritis (GCA) patients were randomized to receive either tocilizumab or placebo in addition to glucocorticoids. We used a multivariable mixed-effects model to evaluate associations of HbA1c level with daily glucocorticoid dose, randomization to receive tocilizumab, and red blood cell count in patients with and those without diabetes mellitus at baseline, over 52 weeks. RESULTS: In 209 patients, the median HbA1c level decreased by 0.50% (P < 0.01) in the group that received both tocilizumab and glucocorticoids (tocilizumab/glucocorticoid) and by 0.10% (P < 0.01) in the glucocorticoid-only group. Randomization to tocilizumab/glucocorticoid was associated with lower HbA1c (ß = -0.209% in those without diabetes, P < 0.01; ß = -0.290% in those with diabetes, P = 0.23). These changes had a sizable impact on glucose tolerance classification: 42.5% of patients in the tocilizumab/glucocorticoid group improved from prediabetes status to normal, compared to only 12.5% of patients treated with glucocorticoids alone. Daily glucocorticoid dose was associated with HbA1c level in patients with baseline diabetes (ß = 0.018%/mg, P < 0.01) and those without baseline diabetes (ß = 0.005%/mg, P < 0.01). CONCLUSION: Tocilizumab treatment was associated with a substantial reduction in HbA1c level, independent of glucocorticoid exposure, which may be achieved through a combination of glycemic and nonglycemic effects.

Hematologic setpoints are a stable and patient-specific deep phenotype.

The complete blood count is an important screening tool for healthy adults and is the most commonly ordered test at periodic physical exams. However, results are usually interpreted relative to one-size-fits-all reference intervals, undermining the goal of precision medicine to tailor medical care to the needs of individual patients based on their unique characteristics. Here we show that standard complete blood count indices in healthy adults have robust homeostatic setpoints that are patient-specific and stable, with the typical healthy adult's set of 9 blood count setpoints distinguishable from 98% of others, and with these differences persisting for decades. These setpoints reflect a deep physiologic phenotype, enabling improved detection of both acquired and genetic determinants of hematologic regulation, including discovery of multiple novel loci via GWAS analyses. Patient-specific reference intervals derived from setpoints enable more accurate personalized risk assessment, and the setpoints themselves are significantly correlated with mortality risk, providing new opportunities to enhance patient-specific screening and early intervention. This study shows complete blood count setpoints are sufficiently stable and patient-specific to help realize the promise of precision medicine for healthy adults.

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets.

Permutation invariant neural networks are a promising tool for making predictions from sets. However, we show that existing permutation invariant architectures, Deep Sets and Set Transformer, can suffer from vanishing or exploding gradients when they are deep. Additionally, layer norm, the normalization of choice in Set Transformer, can hurt performance by removing information useful for prediction. To address these issues, we introduce the "clean path principle" for equivariant residual connections and develop set norm (sn), a normalization tailored for sets. With these, we build Deep Sets++ and Set Transformer++, models that reach high depths with better or comparable performance than their original counterparts on a diverse suite of tasks. We additionally introduce Flow-RBC, a new single-cell dataset and real-world application of permutation invariant prediction. We open-source our data and code here: https://github.com/rajesh-lab/deep_permutation_invariant.

Where Do We Stand in Regularization for Life Science Studies?

More and more biologists and bioinformaticians turn to machine learning to analyze large amounts of data. In this context, it is crucial to understand which is the most suitable data analysis pipeline for achieving reliable results. This process may be challenging, due to a variety of factors, the most crucial ones being the data type and the general goal of the analysis (e.g., explorative or predictive). Life science data sets require further consideration as they often contain measures with a low signal-to-noise ratio, high-dimensional observations, and relatively few samples. In this complex setting, regularization, which can be defined as the introduction of additional information to solve an ill-posed problem, is the tool of choice to obtain robust models. Different regularization practices may be used depending both on characteristics of the data and of the question asked, and different choices may lead to different results. In this article, we provide a comprehensive description of the impact and importance of regularization techniques in life science studies. In particular, we provide an intuition of what regularization is and of the different ways it can be implemented and exploited. We propose four general life sciences problems in which regularization is fundamental and should be exploited for robustness. For each of these large families of problems, we enumerate different techniques as well as examples and case studies. Lastly, we provide a unified view of how to approach each data type with various regularization techniques.

A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease.

Genome-wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer's disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD.

Secondary Somatic Mutations in G-Protein-Related Pathways and Mutation Signatures in Uveal Melanoma.

BACKGROUND: Uveal melanoma (UM), a rare cancer of the eye, is characterized by initiating mutations in the genes G-protein subunit alpha Q (GNAQ), G-protein subunit alpha 11 (GNA11), cysteinyl leukotriene receptor 2 (CYSLTR2), and phospholipase C beta 4 (PLCB4) and by metastasis-promoting mutations in the genes splicing factor 3B1 (SF3B1), serine and arginine rich splicing factor 2 (SRSF2), and BRCA1-associated protein 1 (BAP1). Here, we tested the hypothesis that additional mutations, though occurring in only a few cases ("secondary drivers"), might influence tumor development. METHODS: We analyzed all the 4125 mutations detected in exome sequencing datasets, comprising a total of 139 Ums, and tested the enrichment of secondary drivers in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that also contained the initiating mutations. We searched for additional mutations in the putative secondary driver gene protein tyrosine kinase 2 beta (PTK2B) and we developed new mutational signatures that explain the mutational pattern observed in UM. RESULTS: Secondary drivers were significantly enriched in KEGG pathways that also contained GNAQ and GNA11, such as the calcium-signaling pathway. Many of the secondary drivers were known cancer driver genes and were strongly associated with metastasis and survival. We identified additional mutations in PTK2B. Sparse dictionary learning allowed for the identification of mutational signatures specific for UM. CONCLUSIONS: A considerable part of rare mutations that occur in addition to known driver mutations are likely to affect tumor development and progression.