Salomons, Gajja S.

AbstractAnalyses of 19 amino acids, 38 acylcarnitines, and 3 creatine analogues (https://clir.mayo.edu) were implemented to test the hypothesis that succinic semialdehyde dehydrogenase deficiency (SSADHD) could be identified in dried bloodspots (DBS) using currently available newborn screening methodology. The study population included 17 post‐newborn SSADHD DBS (age range 0.8‐38 years; median, 8.2 years; 10 M; controls, 129‐353 age‐matched individuals, mixed gender) and 10 newborn SSADHD DBS (including first and second screens from 3 of 7 patients). Low (informative) markers in post‐newborn DBS included C2‐ and C4‐OH carnitines, ornithine, histidine and creatine, with no gender differences. For newborn DBS, informative markers included C2‐, C3‐, C4‐ and C4‐OH carnitines, creatine and ornithine. Of these, only creatine demonstrated a significant change with age, revealing an approximate 4‐fold decrease. We conclude that quantitation of short‐chain acylcarnitines, creatine, and ornithine provides a newborn DBS profile with potential as a first tier screening tool for early detection of SSADHD. This first tier evaluation can be readily verified using a previously described second tier liquid chromatography‐tandem mass spectrometry method for γ‐hydroxybutyric acid in the same DBS. More extensive evaluation of this first/second tier screening approach is needed in a larger population.

Abstract BACKGROUND Urinary concentrations of creatine and guanidinoacetic acid divided by creatinine are informative markers for cerebral creatine deficiency syndromes (CDSs). The renal excretion of these substances varies substantially with age and sex, challenging the sensitivity and specificity of postanalytical interpretation. METHODS Results from 155 patients with CDS and 12 507 reference individuals were contributed by 5 diagnostic laboratories. They were binned into 104 adjacent age intervals and renormalized with Box–Cox transforms (Ξ). Estimates for central tendency (μ) and dispersion (σ) of Ξ were obtained for each bin. Polynomial regression analysis was used to establish the age dependence of both μ[log(age)] and σ[log(age)]. The regression residuals were then calculated as z-scores = {Ξ − μ[log(age)]}/σ[log(age)]. The process was iterated until all z-scores outside Tukey fences ±3.372 were identified and removed. Continuous percentile charts were then calculated and plotted by retransformation. RESULTS Statistically significant and biologically relevant subgroups of z-scores were identified. Significantly higher marker values were seen in females than males, necessitating separate reference intervals in both adolescents and adults. Comparison between our reconstructed reference percentiles and current standard age-matched reference intervals highlights an underlying risk of false-positive and false-negative events at certain ages. CONCLUSIONS Disease markers depending strongly on covariates such as age and sex require large numbers of reference individuals to establish peripheral percentiles with sufficient precision. This is feasible only through collaborative data sharing and the use of appropriate statistical methods. Broad application of this approach can be implemented through freely available Web-based software.