When mind and measurement diverge; the interplay between subjective cognitive complaints (SCCs), objective cognition, age, and depression in autistic adults.

While the increased incidence of dementia and subjective cognitive complaints (SCCs) suggests that autistic adults may face cognitive challenges at older age, the extent to which SCCs predict (future) cognitive functioning remains uncertain. This uncertainty is complicated by associations with variables like depression. The current study aims to unravel the interplay of age, depression, cognitive performance, and SCCs in autism. Using a large cross-sectional cohort of autistic (n=202) and non-autistic adults (n=247), we analyzed associations of SCCs with age, depression, and cognitive performance across three domains (visual memory, verbal memory, and fluency). Results showed a strong significant association between depression and SCCs in both autistic and non-autistic adults. Cognitive performance was not significantly associated with SCCs, except for a (modest) association between visual memory performance and SCCs in autistic adults only. Follow-up regression tree analysis indicated that depression and being autistic were considerably more predictive of SCCs than objective cognitive performance. Age nor sex was significantly associated with SCCs. These findings indicate that self-reported cognitive functioning does not equal cognitive performance, and should be interpreted with care, especially in individuals with high rates of depression. Longitudinal investigations are needed to understand SCCs' role in dementia and cognitive health in autism.

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.

Remembering the future; prospective memory across the autistic adult's life span.

LAY ABSTRACT: What is already known: Prospective memory is an important function for daily living. It is the cognitive function that helps you remember that you are meeting your friend for coffee at 2 pm tomorrow, or that you need to take your vitamins after breakfast. This cognitive function is particularly important in autistic adults, but how prospective memory is associated with increasing age, we currently do not know.What this paper adds: Although performance on experimental tasks that measure prospective memory decreases with age, this pattern is similar in autistic and non-autistic adults. No age effects were found for tasks that were performed outside the lab. Autistic adults and non-autistic adults perform similarly on prospective memory, and this performance remains similar when autistic and non-autistic adults age.Implications for practice, research, or policy: While our results show that prospective memory decreased with increasing age, our results do point to parallel development of prospective memory in autistic and non-autistic adults. This finding serves as a reassurance for those individuals concerned that older autistic individuals might show quicker cognitive decline.

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.

One size does not fit all: An individualized approach to understand heterogeneous cognitive performance in autistic adults.

Cognitive performances of autistic people vary widely. Therefore, previous group-based comparisons on cognitive aging in autistic adults might have overlooked those autistic adults that are particularly vulnerable for cognitive decline. Multivariate normative comparisons (MNC) statistically assess individual cognitive differences on the entire cognitive profile. Cognitive deviancy as indicated by MNC accurately predicts future cognitive decline, and is therefore sensitive in detecting meaningful cognitive differences. The current study aimed to (1) investigate the applicability of MNC to assess cognitive performance in autism individually, and (2) understand heterogeneous cognitive performance in autistic adults. As pre-registered, we performed MNC in a sample of 254 non-autistic adults, and two independent samples of respectively 118, and 86 autistic adults (20-85 years, mean: 50 years). Cognitive performance was measured on 11 outcomes in six domains (verbal/visual memory, working memory, verbal fluency, Theory of Mind, and psychomotor speed). Using MNC, about twice as many autistic individuals had a deviant cognitive profile (i.e., deviated statistically from the multivariate normspace) as compared to non-autistic individuals. Importantly, most autistic individuals (>80%) did not have a deviant cognitive profile. Having a deviant profile was significantly associated with higher levels of psychological distress in autistic adults specifically, showing the clinical relevance of this method. Therefore, MNC seem a useful tool to individually detect meaningful cognitive differences in autism. These results are consistent with previous cognitive studies suggesting that most autistic adults show fairly similar cognitive profiles to non-autistic adults, yet highlight the necessity for approaches reflecting the heterogeneity observed in autistic people.

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.

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.

Menstruation and menopause in autistic adults: Periods of importance?

LAY ABSTRACT: Autism spectrum conditions were once seen as a predominantly male condition, but this has caused research to have little focus on women. Therefore, little is known about menstruation and menopause in autism spectrum conditions. Some smaller studies indicate that autistic individuals might suffer from increased difficulties surrounding these events. This study aimed to investigate whether autistic women experience more frequent premenstrual dysphoric disorder, causing extreme physical, emotional, and functional impairment. In a partly overlapping sample, we also examined whether women with autism spectrum condition experience increased complaints surrounding menopause. We did not find an increased prevalence of premenstrual dysphoric disorder in autism spectrum conditions (14.3%) compared with non-autistic women (9.5%). Those with autism spectrum conditions did experience increased menopausal complaints. These menopausal complaints were associated with higher levels of depression and autistic traits. In non-autistic women, menopausal complaints were associated with increased inattention, hyperactivity/impulsivity (i.e. attention deficit hyperactivity disorder traits), and depression. With this work, we show the important role that major reproductive milestones can have in an autistic woman's life.

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.

Subgroups in Late Adulthood Are Associated With Cognition and Wellbeing Later in Life.

Objectives: In this study, we aim to discover whether there are valid subgroups in aging that are defined by modifiable factors and are determinant of clinically relevant outcomes regarding healthy aging. Method: Data from interviews were collected in the Longitudinal Aging Study Amsterdam at two measurement occasions with a 3-year interval. Input for the analyses were seven well-known vulnerability and protective factors of healthy aging. By means of community detection, we tested whether we could distinguish subgroups in a sample of 1478 participants (T1-sample, aged 61-101 years). We tested both the external validity (T1) and predictive validity (T2) for wellbeing and subjective cognitive decline. Moreover, replicability and long-term stability were determined in 1186 participants (T2-sample, aged 61-101 years). Results: Three similar subgroups were identified at T1 and T2. Subgroup A was characterized by high levels of education with personal vulnerabilities, subgroup B by being physically active with low support and low levels of education, and subgroup C by high levels of support with low levels of education. Subgroup C showed the lowest wellbeing and memory profile, both at T1 and T2. On most measures of wellbeing and memory, subgroups A and B did not differ from each other. At T2, the same number of subgroups was identified and subgroup profiles at T1 and T2 were practically identical. Per T1 subgroup 47-62% retained their membership at T2. Discussion: We identified valid subgroups that replicate over time and differ on external variables at current and later measurement occasions. Individual change in subgroup membership over time shows that transitions to subgroups with better outcomes are possible.