The position of geochemical variables as causal co-factors of diseases of unknown aetiology

Abstract

The term diseases of unknown aetiology (DUA) or idiopathic diseases is used to describe diseases that are of uncertain or unknown cause or origin. Among plausible geoenvironmental co-factors in causation of DUA, this article focusses on the entry of trace elements, including metals and metalloids into humans, and their involvement in humoral and cellular immune responses, representing potentially toxic agents with implications as co-factors for certain DUA. Several trace elements/metals/metalloids (micronutrients) play vital roles as co-factors for essential enzymes and antioxidant molecules, thus, conferring protection against disease. However, inborn errors of trace element/metal/metalloid metabolisms can occur to produce toxicity, such as when there are basic defects in the element transport mechanism. Ultimately, it is the amount of trace element, metal or metalloid that is taken up, its mode of accumulation in human tissues, and related geomedical attributes such as the chemical form and bioavailability that decisively determine whether the exerted effects are toxic or beneficial. Several case descriptions of DUA that are common worldwide are given to illustrate our knowledge so far of how trace element/metal/metalloid interactions in the immune system may engender its dysregulation and be implicated as causal co-factors of DUA.

Article highlights

The importance of a proper understanding of geochemical perturbations in human metabolisms is emphasisedIt is proferred that such an understanding would aid greatly in the decipherment of diseases of unknown aetiology (DUA)The thesis presented may pave the way towards better diagnosis and therapy of DUA.

A Bayesian Functional Methodology for Dengue Risk Mapping in Latin America and the Caribbean

Dengue fever has become one of the most outstanding infectious diseases in the world. Besides, the incidence and prevalence of dengue are increasing in the endemic areas of the tropical and subtropical regions. Space and time disease mapping models are common instruments to explain the patterns of disease counts, where hierarchical Bayesian models constitute a suitable framework for their formulation. These random events reflect interactions between nearby geographic locations, as well as correlations between close temporary instants. Functional data analysis techniques can better describe the evolution of disease mapping. In this paper, the risk of dengue in Mexico, Central and South America is studied from a Functional approach through a Bayesian estimation model focused on Hilbert-valued autoregressive processes combined with the Kalman filtering algorithm. Thus, the temporal functional evolution of spatial geographic patterns of incidence risk in disease mapping during 1998-2018 is approximated. Applying this methodology, the excess of smoothing that occurs with traditional models is avoided and the heterogeneity is conserved across the years. It improves the number of false positives created by noise and the number of false negatives as well. The results obtained with the application of this model are compared with those of previous models, corroborating the preceding statements and obtaining better results in the relative risk estimates, providing greater robustness and stability of disease risk estimates.