Abstract Allostatic load scores (ALSs) quantify the cumulative physiological burden of sustained stress across neuro‐endocrine, metabolic, cardiovascular and inflammatory domains; however ALS is heterogeneous in nature. Using domain‐specific urinary metabolomic signatures may improve evaluation accuracy, provide an innovative alternative to stress characterization, identify early domain‐specific perturbations and allow comparative investigations. We designed and evaluated a novel, multilayered perceptron neural network (MLP‐NN) method to investigate metabolic perturbations reflecting domain‐specific alterations in low and high sustained stress, measured by ALS, and described ALS domain‐specific metabolomic profiles. Data from 955 South Africans were used. ALS was calculated from dehydroepiandrosterone (DHEA), adrenocorticotropic hormone (ACTH), cortisol, interleukin‐6 (IL‐6), C‐reactive protein (CRP), waist circumference (WC), glycated haemoglobin (HbA1c), blood‐pressure and high‐density lipoprotein cholesterol. Urinary amino acids and acylcarnitines ( N = 32 metabolites) were analysed using liquid chromatography‐tandem‐mass‐spectrometry. MLP‐NN assessed metabolite contribution to the allostatic load (AL) domains, controlling for confounders, identifying the main metabolites, per AL domain. The median ALS was 3, with high stress (ALS ≥ 4) observed in 30% of participants. Significant differences were observed across all 32 metabolites between high and low ALS groups (all P < 0.05). MLP‐NN revealed distinct domain‐specific metabolomic patterns in low and high ALS. In low ALS the neuro‐endocrine, cardiovascular and metabolic domains showed metabolomic signatures reflective of normal physiology. However in high ALS, metabolomic profiles reflected compensatory mechanisms linking neurotransmitter synthesis, redox balance and energy metabolism, mainly in the neuroendocrine, inflammatory and metabolic domains. This novel MLP‐NN‐based approach identified unique urinary profiles reflective of higher AL, independent of traditional confounders. This non‐invasive approach may serve as an alternative for assessing AL, retaining domain‐specificity, yet allowing comparative studies without the heterogeneity of traditional ALS. image Key points Allostatic load scores (ALSs) are heterogenous, complicating cross‐study comparisons and clinical inferences. Determining allostatic load domain‐specific metabolomic profiles using neural networks may identify early changes in specific domains. This study designed and evaluated a novel neural network‐based model determining allostatic‐load‐domain‐specific metabolomic signatures in high and low sustained stress. This novel neural‐network‐based approach, combinedly analysing metabolomic data and ALS domain‐specific patterns, identified unique urinary profiles, independent of traditional confounders. This non‐invasive approach may serve as an alternative for assessing AL, retaining the domain‐specificity, identifying early domain‐related perturbations related to sustained stress and allowing comparative studies.
Joubert et al. (Sat,) studied this question.