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Background: Rigid age limits in the current allocation system for post-mortem donor kidneys in Germany may have problematic effects. The new German national transplantion registry enables data analysis with respect to this question. Methods: Using anonymized data from the German national transplantion registry, we extracted and evaluated information on the recipients and postmortem donors of kidneys that were allocated in Germany through Eurotransplant over the period 2006-2020. Results: Data on 19 664 kidney transplantations in Germany from 2006 to 2020 were analyzed. The median waiting time for kidney transplantation was 5.8 years. Persons under age 18 waited a median of 1.7 years; persons aged 18 to 64, 7.0 years; and persons aged 65 and older, 3.8 years. Over the period of observation, post-mortem kidneys were transplanted into 401 people of age 64 (2.0% of all organ recipients) and 1,393 people of age 65 (7.1% of all organ recipients). The difference in waiting times between allocation programs for persons under age 65 (ETKAS, „Eurotransplant Kidney Allocation System“) and those aged 65 and older (ESP, „Eurotransplant Senior Program“) increased over the period of observation, from 2.6 years in 2006-2010 to 4.1 years in 2017-2020. Conclusion: The rigid age limits in the current allocation rules for post-mortem kidney donations in Germany are prolonging the waiting times for transplants among patients aged 18 to 64. We think these rules need to be fundamentally reassessed. [ABSTRACT FROM AUTHOR]

Motivation: In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed. Results: Here, we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets. [ABSTRACT FROM AUTHOR]