Inflammation Thread Runs across Medical Laboratory Specialities

Abstract

We work on the assumption that four major specialities or sectors of medical laboratory assays, comprising clinical chemistry, haematology, immunology, and microbiology, embraced by genome sequencing techniques, are routinely in use. Medical laboratory markers for inflammation serve as model: they are allotted to most fields of medical lab assays including genomics. Incessant coding of assays aligns each of them in the long lists of big data. As exemplified with the complement gene family, containing C2, C3, C8A, C8B, CFH, CFI, and ITGB2, heritability patterns/risk factors associated with diseases with genetic glitch of complement components are unfolding. The C4 component serum levels depend on sufficient vitamin D whilst low vitamin D is inversely related to IgG1, IgA, and C3 linking vitamin sufficiency to innate immunity. Whole genome sequencing of microbial organisms may distinguish virulent from nonvirulent and antibiotic resistant from nonresistant varieties of the same species and thus can be listed in personal big data banks including microbiological pathology; the big data warehouse continues to grow.

Figure 1

Metabolome profiling. Real-time metabolome profiling by injection of living bacteria, yeast, or mammalian cells into a high-resolution mass spectrometer enabling automated monitoring of several hundred metabolites can be simultaneously quantitatively estimated within minutes in a circuitry displayed in the work published by the Swiss Federal Institute of Technology. Output-fluxes of ~300 compounds using automated monitoring in 15–30 s cycles over several hours are possible. The figure is a simplified transposition of single data points from hundreds of possible analyses (squares, randomly highlighted with colors) becoming linked using bioinformatics into series and categories meaningful for exploration [29].

Figure 2

The four main specialities of laboratory medical analyses. The four major sections of medical laboratory analyses are shown using a four-box pattern, that is, clinical chemistry, haematology, immunology, and microbiology. Approximate quota of humoral and cellular assays is given. The big data data warehouse is substantially enlarged if each assay is completed/translated using DNA testing by real-time polymerase chain reaction (RT-PCR). Genetic embracement is drawn as broken line.

Figure 3

Complement component C4 levels depend on vitamin D sufficiency. With increasing vitamin D levels up to sufficiency (abscissa), the C4 serum levels statistically significantly increase in this cohort of healthy senior study participants > 60 years of age. From Sakem et al. 2013 [60]. A cohort of 1,470 healthy Swiss men and women, 60 years or older, were recruited for this study. A total of 179 subjects dropped out of the study because of elevated serum concentrations of C-reactive protein (>5 mg/L) making occult inflammation suspicious. 25(OH) vitamin D was measured using HPLC and levels were corroborated by parathyroid hormone measurements (not shown). The C4 levels were measured using immunonephelometry.

Figure 4

Analyses from different sections of the medical laboratory must be pulled together to establish evidence for inflammation. Similar to the display in Figure 2, the 4 major specialities, clinical chemistry, haematology, immunology, and microbiology, each encompass a whole array of inflammation markers. Networking across lab specialities obtrudes to pin down inflammatory disturbance. Complement analytics placed in the “immunology” box. Red bars connect strong evidence, while orange bars show medium evidence for denoted disease states. The insinuated disease states are placed arbitrarily.