Autism spectrum disorder and kidney disease

Clothier, Joanna; Absoud, Michael

doi:10.1007/s00467-020-04875-y

Cited by 15 publications

(12 citation statements)

References 42 publications

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“…Within body-site clustering of fecal meta-signatures of 34 different conditions, studied by at least 2 studies in BugSigDB and generated from 504 signatures of increased relative abundance in the study group, revealed similarities in reported differential abundance patterns between disease phenotypes (Figure 5B). This included similarities in microbial shifts for (i) HIV infection and different gastrointestinal cancers, both characterized by chronic inflammation of the GI tract and microbial signatures that point to shared pathogenic pathways including tryptophan catabolism and butyrate synthesis 79 , (ii) chronic kidney disease and autism, linked through deleterious copy number variants 80,81 and pathogenic gut microbiota-derived metabolites produced by species of the Clostridia class 82,83 , and (iii) type 2 diabetes and schizophrenia, consistent with observations that people with schizophrenia are at increased risk of type 2 diabetes, and conversely, that traditional risk factors for type 2 diabetes are common in people with schizophrenia and can affect the gut microbiome, especially obesity, poor diet, and sedentary lifestyle 84 . A strong enrichment of genera of the Clostridia class drove the similarity between the meta-signatures of Hashimoto’s thyroiditis (12 out of 14 genera, 85.7%, p -value = 2.1 · 10 −05 , proportion test) and chronic fatigue syndrome (11/17, 64.7%, p -value = 0.003).…”

Section: Resultsmentioning

confidence: 99%

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

Mirzayi

Zohra

Azhar

et al. 2022

Preprint

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The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies, accompanied by information on study geography, health outcomes, host body site, and experimental, epidemiological, and statistical methods using controlled vocabulary. BugSigDB is seeded for initial release with >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and co-exclusion, and consensus signatures, allowing assessment of microbiome differential abundance within and across experimental conditions, environments, or body sites. Database-wide analysis revealed experimental conditions with the highest level of consistency in signatures reported by independent studies and identified commonalities among disease-associated signatures including frequent introgression of oral pathobionts into the gut.

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Section: Resultsmentioning

confidence: 99%

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

Mirzayi

Zohra

Azhar

et al. 2022

Preprint

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“…Furthermore, we have previously shown that kidney disease was associated with cardiovascular risk factors in our study ASD + ID population (Miot et al, 2019). In addition, several recent studies have highlighted putative genetic vulnerability to kidney disease in ASD as early as in childhood (Clothier & Absoud, 2021), which may be compounded by cardiovascular diseases and ageing itself.…”

Section: Discussionmentioning

confidence: 99%

Multimorbidity patterns and subgroups among autistic adults with intellectual disability: Results from the EFAAR study

Miot

Chancel

Peries

et al. 2022

Autism

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Multimorbidity, defined as having two or more chronic health conditions, is associated with elevated polypharmacy and mortality. Autism spectrum disorder is a whole-body chronic health condition in which comorbidities – in particular co-occurring intellectual disability – contribute to high clinical heterogeneity, polypharmacy and premature mortality. We aimed to determine specific multimorbidity patterns among autism spectrum disorder + intellectual disability adults, and to identify participants’ subgroups based on multimorbidity features. We used baseline examination data from a previous exploratory prospective multicentric study that included 63 autism spectrum disorder + intellectual disability adults. Multimorbidity patterns and subgroups were determined using clustering approaches. We observed 84.1% multimorbidity, significantly associated with age. We identified a dominant multimorbidity pattern, combining immune dysfunction, gastrointestinal disorders, neurological, and joint diseases. Four participants’ subgroups could be distinguished by multimorbidity, autonomy and polypharmacy. Two clusters were distinguished by the prevalence and consequences of multimorbidity. One cluster involved women with endocrine disorders. The final cluster was composed of older adults with the lowest autism spectrum disorder severity but greater multimorbidity, including cardiovascular and kidney diseases. Our results support a role for the gut–brain axis in the pathophysiology of autism spectrum disorder + intellectual disability multimorbidity. Furthermore, we identified patient subgroups with specific needs, underscoring the importance of a holistic approach for autism spectrum disorder + intellectual disability adults. Lay abstract Multimorbidity relates to having multiple chronic health conditions. It is a risk factor for poor health and reduces life expectancy. Autistic people have multiple chronic health conditions and die prematurely, especially if they have an intellectual disability (autism spectrum disorder and intellectual disability). Certain pathophysiological processes observed in autism spectrum disorder are common to those related to the genesis and/or maintenance of multimorbidity. Furthermore, multimorbidity could be helpful in better identifying patient subgroups in autism spectrum disorder. It is therefore essential to better characterize multimorbidity and its consequences in the subgroup of autism spectrum disorder + intellectual disability individuals to offer them personalized care. We conducted a preliminary study of 63 autism spectrum disorder + intellectual disability adults to classify them according to their multimorbidity and search for a specific combination of chronic health conditions. We observed high and early multimorbidity in this sample and identified four classes of participants, distinguished by their multimorbidity status, independence and number of treatments. In addition, we observed a dominant combination of multimorbidity in our sample, combining immune dysfunction and gastrointestinal disorders, neurological and joint diseases. These findings support the hypothesis that an altered gut–brain relationship is involved in the risk of autism spectrum disorder, its outcome, and its association with chronic health conditions. Although larger studies are needed, our results suggest that subgroups of autism spectrum disorder + intellectual disability individuals can be identified based on their multimorbidity and potentially different ageing trajectories. A more comprehensive and personalized approach is needed to reduce the burden of multimorbidity and increase the quality of life and life expectancy in autism spectrum disorder/ intellectual disability.

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“…Of note, seven DEGs (Crym, Ctss, Fasn, Fcgrt, Gabrb3, Lyz2, and Vcam1) were found to be associated with kidney diseases, including renal inflammation, crescentic glomerulonephritis or end-stage renal failure. Interestingly, population-based studies support that autism spectrum disorder (ASD) and kidney disease coexist in several genetic disorders (for review see Table 2 in (33)), suggesting that the same genetic modification can affect neurodevelopment and nephrogenesis. However, because the genetic alterations (deletion or duplication) associated with these disorders often encompass several genes, it is still unclear how these genes contribute to the underlying molecular mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Haploinsufficiency of the mouseTshz3gene leads to kidney defects

Sanchez-Martin¹,

Magalhães²,

Ranjzad³

et al. 2021

Human Molecular Genetics

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Renal tract defects and autism spectrum disorder (ASD) deficits represent the phenotypic core of the 19q12 deletion syndrome caused by the loss of one copy of the TSHZ3 gene. While a proportion of Tshz3 heterozygous (Tshz3+/lacZ) mice display ureteral defects, no kidney defects have been reported in these mice. The purpose of this study was to characterize the expression of Tshz3 in adult kidney as well as the renal consequences of embryonic haploinsufficiency of Tshz3 by analyzing the morphology and function of Tshz3 heterozygous adult kidney. Here, we described Tshz3 expression in the smooth muscle and stromal cells lining the renal pelvis, the papilla and glomerular endothelial cells (GEnCs) of the adult kidney as well as in the proximal nephron tubules in neonatal mice. Histological analysis showed that Tshz3+/lacZ adult kidney had an average of 29% fewer glomeruli than wild type kidney. Transmission electron microscopy (TEM) of Tshz3+/lacZ glomeruli revealed a reduced thickness of the glomerular basement membrane and a larger foot process width. Compared to wild type, Tshz3+/lacZ mice showed lower blood urea, phosphates, magnesium and potassium at 2 months of age. At the molecular level, transcriptome analysis identified differentially expressed genes related to inflammatory processes in Tshz3+/lacZ compare to wild type (WT; control) adult kidneys. Lastly, analysis of the urinary peptidome revealed 33 peptides associated with Tshz3+/lacZ adult mice. These results provide the first evidence that in the mouse Tshz3 haploinsufficiency leads to cellular, molecular and functional abnormalities in the adult mouse kidney.

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Autism spectrum disorder and kidney disease

Cited by 15 publications

References 42 publications

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

Multimorbidity patterns and subgroups among autistic adults with intellectual disability: Results from the EFAAR study

Haploinsufficiency of the mouseTshz3gene leads to kidney defects

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