The clinical relevance of subgroups of autistic adults: Stability and predictive value.

Autism in adulthood is characterized by heterogeneity, complicating the provision of tailored support. In previous work, we aimed to capture this heterogeneity by determining subgroups of autistic adults that differed in clinical outcomes: cognitive failures, psychological difficulties, and quality of life (QoL). Two subgroups were identified: a "Feelings of Low Grip" subgroup characterized by experiencing a lower sense of mastery and a higher susceptibility to difficulties in daily life, and a "Feelings of High Grip" subgroup characterized by a higher sense of mastery and lower susceptibility to difficulties in daily life. The current pre-registered study involves a longitudinal extension to determine (a) stability and (b) predictive value of the previously identified two subgroups. Subgroups were identified using community detection based on 14 self-report measures related to demographic, psychological, and lifestyle characteristics in two samples (aged 31-86 years) that were analyzed separately: Sample 1 (NAutism = 80) measured 5 years after baseline and Sample 2 (NAutism = 241, NComparison = 211) measured 2 years after baseline. The stability over time was assessed based on (a) the number of subgroups, (b) subgroup profiles, and (c) subgroup membership. Predictive validity was assessed for cognitive failures, psychological difficulties, and QoL. Results indicated that autistic and non-autistic adults formed distinct subgroups. Within both autism samples, the two previously identified autism subgroups were replicated at follow-up. Subgroup profiles were similar for >50% of the variables at two-year follow-up, and 21% at five-year follow-up. Moreover, ≥76% remained in the same subgroup at two-year follow-up, and ≥ 57% after 5 years. Subgroup membership was predictive of external clinical outcomes up to 5 years. Thus, this study demonstrated the stability and predictive value of the autism subgroups, especially for the two-year follow-up. A further focus on their clinical utility might increase the aptness of support, and may provide more insight into the aging process when being autistic.

The relationship between camouflaging and mental health: Are there differences among subgroups in autistic adults?

LAY ABSTRACT: When autistic people use strategies to hide their autistic characteristics, we call this camouflaging. Autistic adults suggested that camouflaging can result in mental health difficulties. That is, people who report to camouflage also report mental health difficulties. However, since there are many differences between autistic people, this relationship may also differ between subgroups. Therefore, in this study we investigated whether camouflaging and mental health difficulties are related and whether this relationship is equal for all autistic adults. For this study, 352 autistic adults aged 30-84 years filled in the Dutch Camouflaging Autistic Traits Questionnaire to measure camouflaging and the Symptom Checklist-90 Revised to measure mental health difficulties. We found that camouflaging was moderately related to mental health difficulties. This means that people who report more camouflaging also report more mental health difficulties. When we looked closer, we found that this relationship was strong for only a small subgroup of autistic adults. In most other autistic adults, there was a small or no relationship between camouflaging and mental health difficulties. Therefore, it is important that clinicians are aware of camouflaging and its possible relationship with mental health difficulties, but that they do not generalize the negative consequences to everyone.

Comparison of network structures between autistic and non-autistic adults, and autism subgroups: A focus on demographic, psychological, and lifestyle factors.

LAY ABSTRACT: There are large differences in the level of demographic, psychological, and lifestyle characteristics between autistic and non-autistic adults but also among autistic people. Our goal was to test whether these differences correspond to differences in underlying relationships between these characteristics-also referred to as network structure-to determine which characteristics (and relationships between them) are important. We tested differences in network structure in (1) autistic and non-autistic adults and (2) two previously identified subgroups of autistic adults. We showed that comparing networks of autistic and non-autistic adults provides subtle differences, whereas networks of the autism subgroups were similar. There were also no sex differences in the networks of the autism subgroups. Thus, the previously observed differences in the level of characteristics did not correspond to differences across subgroups in how these characteristics relate to one another (i.e. network structure). Consequently, a focus on differences in characteristics is not sufficient to determine which characteristics (and relationships between them) are of importance. Hence, network analysis provides a valuable tool beyond looking at (sub)group level differences. These results could provide hints for clinical practice, to eventually determine whether psychological distress, cognitive failures, and reduced quality of life in autistic adults can be addressed by tailored support. However, it is important that these results are first replicated before we move toward intervention or support.

Finding Similarities in Differences Between Autistic Adults: Two Replicated Subgroups.

Autism is heterogeneous, which complicates providing tailored support and future prospects. We aim to identify subgroups in autistic adults with average to high intelligence, to clarify if certain subgroups might need support. We included 14 questionnaire variables related to aging and/or autism (e.g., demographic, psychological, and lifestyle). Community detection analysis was used for subgroup identification in an original sample of 114 autistic adults with an adulthood diagnosis (autism) and 58 non-autistic adults as comparison group (COMP), and a replication sample (NAutism = 261; NCOMP = 287), both aged 30-89 years. Next, we identified subgroups and assessed external validity (for cognitive and psychological difficulties, and quality of life [QoL]) in the autism samples. To test specificity, we repeated the analysis after adding 123 adults with ADHD, aged 30-80 years. As expected, the autism and COMP groups formed distinct subgroups. Among autistic adults, we identified three subgroups of which two were replicated. One of these subgroups seemed most vulnerable on the cluster variables; this subgroup also reported the most cognitive and psychological difficulties, and lowest QoL. Adding the ADHD group did not alter results. Within autistic adults, one subgroup could especially benefit from support and specialized care, although this must be tested in future studies.

Parallel age-related cognitive effects in autism: A cross-sectional replication study.

Findings on age-related cognitive effects in autism in adulthood are inconsistent across studies. As these studies substantially differ in their methodology, replication studies are needed. In this replication study frequentist (i.e., null-hypothesis significance testing), and Bayesian statistics were used to investigate the hypothesis that in autistic adults compared to non-autistic adults mostly parallel, but also protective age-related cognitive effects can be observed. Participants were 88 autistic adults, and 88 non-autistic matched comparisons (age range: 30-89 years, mean age: 55 years). Cognitive measures were administered on the following six domains: verbal memory, visual memory, working memory, Theory of Mind (ToM), verbal fluency, and processing speed, and self-reported cognitive failures. Non-autistic adults outperformed autistic adults on ToM, verbal fluency, and verbal memory, but only the first two were confirmed with Bayesian replication analyses. Also, more cognitive failures were reported by autistic adults. No interactions between group and age were observed, suggesting a parallel age-related effect on all cognitive domains. In sum, previously observed difficulties in ToM and verbal fluency were replicated which seem to persist at older age. Previously reported parallel age-related cognitive patterns were replicated, yet no evidence for protective age-related patterns was found. LAY SUMMARY: We investigated whether our previous findings on cognitive aging in autism could be confirmed in a new study measuring the cognitive effects of age in autistic and non-autistic adults. As expected, tasks that younger autistic adults had difficulties with (theory of mind, fluency) were also difficult for older autistic adults, and the effect of age itself was similar in autistic and non-autistic adults. Unexpectedly, we observed no protective effects (less cognitive aging) in autism.

Ageing and heterogeneity regarding autism spectrum conditions: a protocol paper of an accelerated longitudinal study.

INTRODUCTION: Autism spectrum conditions (ASC) develop early in life and are thought to last a lifetime. However, ASC research has two major knowledge gaps that hinder progression in understanding the concept of ASC and in providing proper support for autistic adults: (1) the majority of knowledge about ASC mainly stems from childhood studies so little is known about older autistic adults and (2) while it is broadly recognised that ASC is a heterogeneous condition, we do not yet understand the differences in trajectories leading to their future outcome. We aim to fill both knowledge gaps. METHODS AND ANALYSIS: A multistage overlapping cohort design assessing (cognitive) ageing in ASC is designed to obtain an accelerated longitudinal data set. Data, including a multitude of questionnaires, diagnostics and cognitive tests, are collected over four waves within a 10-year time frame. This will provide information regarding actual changes in quality of life, co-occurring health conditions and cognition as well as the possibility to test external validity and temporal stability in newly formed behavioural subtypes. Participants consist of three groups of adults aged 20-90 years: (1) with a clinical diagnosis of ASC, (2) with a clinical diagnosis of attention deficit hyperactivity disorder (ADHD) but no ASC, (3) no ASC/ADHD (ie, comparison group). The sample size differs between waves and instruments. Detailed analysis plans will be preregistered in AsPredicted or at the Open Science Framework. ETHICS AND DISSEMINATION: Ethical approval for this study was obtained from the ethical review board of the Department of Psychology of the University of Amsterdam (wave 1 2011-PN-1952 and 2013-PN-2668, wave 2 2015-BC-4270, waves 3 and 4 2018-BC-9285). In line with the funding policies of the grant organisation funding this study, future papers will be published open access.