Metabolische Epilepsien mit spezifischen Therapieoptionen: Diagnostischer Leitfaden

Zusammenfassung: Bei therapieresistenten Anfällen müssen, unabhängig vom jeweiligen Lebensalter, angeborene Stoffwechselerkrankungen erwogen werden. Nur selten liegen hierbei erkennbare Epilepsiesyndrome mit typischem EEG-Muster (EEG: Elektroenzephalographie) oder wegweisende Begleitbefunde in Klinik oder kranialer Bildgebung vor. Für zahlreiche metabolisch bedingte Epilepsien existiert ein kausaler Therapieansatz, z.B. durch gezielte Substitution von Vitaminen, Aminosäuren oder alternativen Energieträgern. Dabei entscheidet ein früher Therapiebeginn wesentlich über das Langzeit-Outcome. Der vorliegende Beitrag soll durch die Beschreibung des klinischen Phänotyps, der Anfallssemiologie sowie der diagnostischen Biomarker und Enzymdefekte einen Leitfaden für die Früherkennung behandelbarer metabolischer Epilepsien im Klinikalltag biete

Genotypic and phenotypic spectrum of pyridoxine-dependent epilepsy (ALDH7A1 deficiency)

Pyridoxine-dependent epilepsy was recently shown to be due to mutations in the ALDH7A1 gene, which encodes antiquitin, an enzyme that catalyses the nicotinamide adenine dinucleotide-dependent dehydrogenation of L-{alpha}-aminoadipic semialdehyde/L-{Delta}1-piperideine 6-carboxylate. However, whilst this is a highly treatable disorder, there is general uncertainty about when to consider this diagnosis and how to test for it. This study aimed to evaluate the use of measurement of urine L-{alpha}-aminoadipic semialdehyde/creatinine ratio and mutation analysis of ALDH7A1 (antiquitin) in investigation of patients with suspected or clinically proven pyridoxine-dependent epilepsy and to characterize further the phenotypic spectrum of antiquitin deficiency. Urinary L-{alpha}-aminoadipic semialdehyde concentration was determined by liquid chromatography tandem mass spectrometry. When this was above the normal range, DNA sequencing of the ALDH7A1 gene was performed. Clinicians were asked to complete questionnaires on clinical, biochemical, magnetic resonance imaging and electroencephalography features of patients. The clinical spectrum of antiquitin deficiency extended from ventriculomegaly detected on foetal ultrasound, through abnormal foetal movements and a multisystem neonatal disorder, to the onset of seizures and autistic features after the first year of life. Our relatively large series suggested that clinical diagnosis of pyridoxine dependent epilepsy can be challenging because: (i) there may be some response to antiepileptic drugs; (ii) in infants with multisystem pathology, the response to pyridoxine may not be instant and obvious; and (iii) structural brain abnormalities may co-exist and be considered sufficient cause of epilepsy, whereas the fits may be a consequence of antiquitin deficiency and are then responsive to pyridoxine. These findings support the use of biochemical and DNA tests for antiquitin deficiency and a clinical trial of pyridoxine in infants and children with epilepsy across a broad range of clinical scenarios

A Causal Framework for Decomposing Spurious Variations

One of the fundamental challenges found throughout the data sciences is to explain why things happen in specific ways, or through which mechanisms a certain variable $X$ exerts influences over another variable $Y$. In statistics and machine learning, significant efforts have been put into developing machinery to estimate correlations across variables efficiently. In causal inference, a large body of literature is concerned with the decomposition of causal effects under the rubric of mediation analysis. However, many variations are spurious in nature, including different phenomena throughout the applied sciences. Despite the statistical power to estimate correlations and the identification power to decompose causal effects, there is still little understanding of the properties of spurious associations and how they can be decomposed in terms of the underlying causal mechanisms. In this manuscript, we develop formal tools for decomposing spurious variations in both Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects and provide sufficient conditions for the identification of such decompositions. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine, and we empirically demonstrate its use on a real-world dataset

Causal Fairness for Outcome Control

As society transitions towards an AI-based decision-making infrastructure, an ever-increasing number of decisions once under control of humans are now delegated to automated systems. Even though such developments make various parts of society more efficient, a large body of evidence suggests that a great deal of care needs to be taken to make such automated decision-making systems fair and equitable, namely, taking into account sensitive attributes such as gender, race, and religion. In this paper, we study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. The interest in such a setting ranges from interventions related to criminal justice and welfare, all the way to clinical decision-making and public health. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision, counterfactually speaking, when contrasted with an alternative, negative one. We introduce the notion of benefit fairness, which can be seen as the minimal fairness requirement in decision-making, and develop an algorithm for satisfying it. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this. Finally, if some of the variations of the protected attribute in the benefit are considered as discriminatory, the notion of benefit fairness may need to be strengthened, which leads us to articulating a notion of causal benefit fairness. Using this notion, we develop a new optimization procedure capable of maximizing $Y$ while ascertaining causal fairness in the decision process

Reconciling Predictive and Statistical Parity: A Causal Approach

Since the rise of fair machine learning as a critical field of inquiry, many different notions on how to quantify and measure discrimination have been proposed in the literature. Some of these notions, however, were shown to be mutually incompatible. Such findings make it appear that numerous different kinds of fairness exist, thereby making a consensus on the appropriate measure of fairness harder to reach, hindering the applications of these tools in practice. In this paper, we investigate one of these key impossibility results that relates the notions of statistical and predictive parity. Specifically, we derive a new causal decomposition formula for the fairness measures associated with predictive parity, and obtain a novel insight into how this criterion is related to statistical parity through the legal doctrines of disparate treatment, disparate impact, and the notion of business necessity. Our results show that through a more careful causal analysis, the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions through the concept of business necessity. Finally, we demonstrate the importance of our findings on a real-world example

Fairness-Accuracy Trade-Offs: A Causal Perspective

Systems based on machine learning may exhibit discriminatory behavior based on sensitive characteristics such as gender, sex, religion, or race. In light of this, various notions of fairness and methods to quantify discrimination were proposed, leading to the development of numerous approaches for constructing fair predictors. At the same time, imposing fairness constraints may decrease the utility of the decision-maker, highlighting a tension between fairness and utility. This tension is also recognized in legal frameworks, for instance in the disparate impact doctrine of Title VII of the Civil Rights Act of 1964 -- in which specific attention is given to considerations of business necessity -- possibly allowing the usage of proxy variables associated with the sensitive attribute in case a high-enough utility cannot be achieved without them. In this work, we analyze the tension between fairness and accuracy from a causal lens for the first time. We introduce the notion of a path-specific excess loss (PSEL) that captures how much the predictor's loss increases when a causal fairness constraint is enforced. We then show that the total excess loss (TEL), defined as the difference between the loss of predictor fair along all causal pathways vs. an unconstrained predictor, can be decomposed into a sum of more local PSELs. At the same time, enforcing a causal constraint often reduces the disparity between demographic groups. Thus, we introduce a quantity that summarizes the fairness-utility trade-off, called the causal fairness/utility ratio, defined as the ratio of the reduction in discrimination vs. the excess loss from constraining a causal pathway. This quantity is suitable for comparing the fairness-utility trade-off across causal pathways. Finally, as our approach requires causally-constrained fair predictors, we introduce a new neural approach for causally-constrained fair learning

Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making

Investigating fairness and equity of automated systems has become a critical field of inquiry. Most of the literature in fair machine learning focuses on defining and achieving fairness criteria in the context of prediction, while not explicitly focusing on how these predictions may be used later on in the pipeline. For instance, if commonly used criteria, such as independence or sufficiency, are satisfied for a prediction score $S$ used for binary classification, they need not be satisfied after an application of a simple thresholding operation on $S$ (as commonly used in practice). In this paper, we take an important step to address this issue in numerous statistical and causal notions of fairness. We introduce the notion of a margin complement, which measures how much a prediction score $S$ changes due to a thresholding operation. We then demonstrate that the marginal difference in the optimal 0/1 predictor $\widehat Y$ between groups, written $P(\hat y \mid x_1) - P(\hat y \mid x_0)$, can be causally decomposed into the influences of $X$ on the $L_2$-optimal prediction score $S$ and the influences of $X$ on the margin complement $M$, along different causal pathways (direct, indirect, spurious). We then show that under suitable causal assumptions, the influences of $X$ on the prediction score $S$ are equal to the influences of $X$ on the true outcome $Y$. This yields a new decomposition of the disparity in the predictor $\widehat Y$ that allows us to disentangle causal differences inherited from the true outcome $Y$ that exists in the real world vs. those coming from the optimization procedure itself. This observation highlights the need for more regulatory oversight due to the potential for bias amplification, and to address this issue we introduce new notions of weak and strong business necessity, together with an algorithm for assessing whether these notions are satisfied

Prevalence of tetrahydrobiopterine (BH4)-responsive alleles among Austrian patients with PAH deficiency: comprehensive results from molecular analysis in 147 patients

Phenylketonuria (PKU, MIM 261600) is an autosomal recessive disorder caused by mutations of the phenylalanine hydroxylase gene (PAH, GenBank U49897.1, RefSeq NM_000277). To date more than 560 variants of the PAH gene have been identified. In Europe there is regional distribution of specific mutations. Due to recent progress in chaperone therapy, the prevalence of BH4-responsive alleles gained therapeutic importance. Here we report the mutational spectrum of PAH deficiency in 147 unrelated Austrian families. Overall mutation detection rate was 98.6%. There was a total of 62 disease-causing mutations, including five novel mutations IVS4 + 6T>A, p.H290Y, IVS8-2A>G, p.A322V and p.I421S. The five most prevalent mutations found in patients were p.R408W, IVS12 + 1G>A, p.R261Q, p.R158Q and IVS2 + 5G>C. Neonatal phenylalanine levels before treatment were available in 114/147 patients. Prediction of BH4-responsiveness in patients with full genotypes was exclusively made according to published data. Among the 133 patients needing dietary treatment, 28.4% are expected to be BH4 "non-responsive", 4.5% are highly likely BH4-responsive, 35.8% are probably BH4-responsive while no interpretation was possible for 31.3%. The mutation data reflect the population history of Austria and provide information on the likely proportion of Austrian PKU patients that may benefit from BH4-therap

The Role of Demographics and Entrepreneurial Motives in Digital Sales Adoption

Digital transformation gradually changes businesses, which is also connected with sales. Despite these advancements, the motives to accept digitalization in sales are undiscovered. In our study, we examine demographic factors (gender, age, education), entrepreneurial motives, and the developmental stage of entrepreneurs using data from the Global Entrepreneurship Monitor (GEM), encompassing 25,633 entrepreneurs from 47 countries. Logistic regression and subsequently Cramer’s V were employed for a more precise determination of the relevant influences of the explanatory variables. The findings indicate that younger and early-stage entrepreneurs more often accept digital technologies in sales. This also applies to entrepreneurs who are motivated by higher income and wealth and the desire to make changes in the world. These results suggest that digital technologies can contribute to reducing the divergence between profitable and sustainable goals, as entrepreneurs perceive their utility in achieving both sets of objectives

FAHN/SPG35 : a narrow phenotypic spectrum across disease classifications

The endoplasmic reticulum enzyme fatty acid 2-hydroxylase (FA2H) plays a major role in the formation of 2-hydroxy glycosphingolipids, main components of myelin. FA2H deficiency in mice leads to severe central demyelination and axon loss. In humans it has been associated with phenotypes from the neurodegeneration with brain iron accumulation (fatty acid hydroxylase-associated neurodegeneration, FAHN), hereditary spastic paraplegia (HSP type SPG35) and leukodystrophy (leukodystrophy with spasticity and dystonia) spectrum. We performed an in-depth clinical and retrospective neurophysiological and imaging study in a cohort of 19 cases with biallelic FA2H mutations. FAHN/SPG35 manifests with early childhood onset predominantly lower limb spastic tetraparesis and truncal instability, dysarthria, dysphagia, cerebellar ataxia, and cognitive deficits, often accompanied by exotropia and movement disorders. The disease is rapidly progressive with loss of ambulation after a median of 7 years after disease onset and demonstrates little interindividual variability. The hair of FAHN/SPG35 patients shows a bristle-like appearance; scanning electron microscopy of patient hair shafts reveals deformities (longitudinal grooves) as well as plaque-like adhesions to the hair, likely caused by an abnormal sebum composition also described in a mouse model of FA2H deficiency. Characteristic imaging features of FAHN/SPG35 can be summarized by the WHAT' acronym: white matter changes, hypointensity of the globus pallidus, ponto-cerebellar atrophy, and thin corpus callosum. At least three of four imaging features are present in 85% of FA2H mutation carriers. Here, we report the first systematic, large cohort study in FAHN/SPG35 and determine the phenotypic spectrum, define the disease course and identify clinical and imaging biomarkers