The human gut microbiome has been associated with many health factors but variability between studies limits exploration of effects between them. Gut microbiota profiles are available for >2700 members of the deeply phenotyped TwinsUK cohort, providing a uniform platform for such comparisons. Here, we present gut microbiota association analyses for 38 common diseases and 51 medications within the cohort. We describe several novel associations, highlight associations common across multiple diseases, and determine which diseases and medications have the greatest association with the gut microbiota. These results provide a reference for future studies of the gut microbiome and its role in human health.

TwinsUK is the largest cohort of community-dwelling adult twins in the UK. The registry comprises over 14,000 volunteer twins (14,838 including mixed, single and triplets); it is predominantly female (82%) and middle-aged (mean age 59). In addition, over 1800 parents and siblings of twins are registered volunteers. During the last 27 years, TwinsUK has collected numerous questionnaire responses, physical/cognitive measures and biological measures on over 8500 subjects. Data were collected alongside four comprehensive phenotyping clinical visits to the Department of Twin Research and Genetic Epidemiology, King’s College London. Such collection methods have resulted in very detailed longitudinal clinical, biochemical, behavioral, dietary and socioeconomic cohort characterization; it provides a multidisciplinary platform for the study of complex disease during the adult life course, including the process of healthy aging. The major strength of TwinsUK is the availability of several ‘omic’ technologies for a range of sample types from participants, which includes genomewide scans of single-nucleotide variants, next-generation sequencing, metabolomic profiles, microbiomics, exome sequencing, epigenetic markers, gene expression arrays, RNA sequencing and telomere length measures. TwinsUK facilitates and actively encourages sharing the ‘TwinsUK’ resource with the scientific community — interested researchers may request data via the TwinsUK website (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/) for their own use or future collaboration with the study team. In addition, further cohort data collection is planned via the Wellcome Open Research gateway (https://wellcomeopenresearch.org/gateways). The current article presents an up-to-date report on the application of technological advances, new study procedures in the cohort and future direction of TwinsUK.

Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological ‘fingerprints’. Such methods have the potential to detect clinically relevant subtypes, track an individual’s disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.

Background and Objectives Alzheimer disease (AD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology. To explore this, we used neuroanatomical normative modeling to index regional patterns of variability in cortical thickness. We aimed to characterize individual differences and outliers in cortical thickness in patients with AD, people with mild cognitive impairment (MCI), and controls. Furthermore, we assessed the relationships between cortical thickness heterogeneity and cognitive function, β-amyloid, phosphorylated-tau, and ApoE genotype. Finally, we examined whether cortical thickness heterogeneity was predictive of conversion from MCI to AD. Methods Cortical thickness measurements across 148 brain regions were obtained from T1-weighted MRI scans from 62 sites of the Alzheimer9s Disease Neuroimaging Initiative. AD was determined by clinical and neuropsychological examination with no comorbidities present. Participants with MCI had reported memory complaints, and controls were cognitively normal. A neuroanatomical normative model indexed cortical thickness distributions using a separate healthy reference data set (n = 33,072), which used hierarchical Bayesian regression to predict cortical thickness per region using age and sex, while adjusting for site noise. Z-scores per region were calculated, resulting in a Z-score brain map per participant. Regions with Z-scores <−1.96 were classified as outliers. Results Patients with AD (n = 206) had a median of 12 outlier regions (out of a possible 148), with the highest proportion of outliers (47%) in the parahippocampal gyrus. For 62 regions, over 90% of these patients had cortical thicknesses within the normal range. Patients with AD had more outlier regions than people with MCI (n = 662) or controls (n = 159) (F(2, 1,022) = 95.39, p = 2.0 × 10⁻¹⁶). They were also more dissimilar to each other than people with MCI or controls (F(2, 1,024) = 209.42, p = 2.2 × 10⁻¹⁶). A greater number of outlier regions were associated with worse cognitive function, CSF protein concentrations, and an increased risk of converting from MCI to AD within 3 years (hazard ratio 1.028, 95% CI 1.016–1.039, p = 1.8 × 10⁻¹⁶). Discussion Individualized normative maps of cortical thickness highlight the heterogeneous effect of AD on the brain. Regional outlier estimates have the potential to be a marker of disease and could be used to track an individual9s disease progression or treatment response in clinical trials.

The preservation of cognitive abilities with aging is a priority both for individuals and nations given the aging populations of many countries. Recently the gut microbiome has been identified as a new territory to explore in relation to cognition. Experiments using rodents have identified a link between the gut microbiome and cognitive function, particularly that low microbial diversity leads to poor cognition function. Similar studies in humans could identify novel targets to encourage healthy cognition in an aging population. Here, we investigate the association of gut microbiota and cognitive function in a human cohort considering the influence of physical frailty. We analyzed 16S rRNA gene sequence data, derived from fecal samples obtained from 1,551 individuals over the age of 40. Cognitive data was collected using four cognitive tests: verbal fluency (n = 1,368), Deary-Liewald Reaction Time Test (DLRT; n = 873), Mini Mental State Examination (recall; n = 1,374) and Paired Associates Learning from the Cambridge Neuropsychological Test Automated Battery (CANTAB-PAL; n = 405). We use mixed effects models to identify associations with alpha diversity, operational taxonomic units (OTUs) and taxa and performed further analyses adjusting for physical frailty. We then repeated the analyses in a subset of individuals with dietary data, also excluding those using medications shown to influence gut microbiome composition. DLRT and verbal fluency were negatively associated with alpha diversity of the gut microbiota (False-Discovery Rate, FDR, p < 0.05). However, when considering frailty as a covariate, only associations between the DLRT and diversity measures remained. Repeating analyses excluding Proton pump inhibitor (PPI) and antibiotic users and accounting for diet, we similarly observe significant negative associations between the DLRT and alpha diversity measures and a further negative association between DLRT and the abundance of the order Burkholderiales that remains significant after adjusting for host frailty. This highlights the importance of considering concurrent differences in physical health in studies of cognitive performance and suggests that physical health has a relatively larger association with the gut microbiome. However, the frailty independent cognitive-gut microbiota associations that were observed might represent important targets for further research, with potential for use in diagnostic surveillance in cognitive aging and interventions to improve vitality.

Frailty is an increased vulnerability to adverse health outcomes, across multiple physiological systems, with both environmental and genetic drivers. The two most commonly used measures are Rockwood’s frailty index (FI) and Fried’s frailty phenotype (FP). the present study included 3626 individuals from the TwinsUK Adult Twin Registry. We used the classical twin model to determine whether FI and FP share the same latent aetiological factors. We also investigated the relationship between frailty and chronic widespread musculoskeletal pain (CWP), another holistic age-related condition with significant clinical impact. FP and FI shared underlying genetic and environmental aetiology. CWP was associated with both frailty measures, and health deficits appeared to mediate the relationship between phenotypic frailty and pain. Latent genetic factors underpinning CWP were shared with frailty. While frailty was increased in the twins reporting pain, co-twin regression analysis indicated that the relationship between CWP and frailty is reduced after accounting for shared genetic and environmental factors. both measures of frailty tap the same root causes, thus this work helps unify frailty research. We confirmed a strong association between CWP and frailty, and showed a large and significant shared genetic aetiology of both phenomena. Our findings argue against pain being a significant causative factor in the development of frailty, favouring common causation. This study highlights the need to manage CWP in frail individuals and undertake a Comprehensive Geriatric Assessment in individuals presenting with CWP. Finally, the search for genetic factors underpinning CWP and frailty could be aided by integrating measures of pain and frailty.