Reproducibility of the Blood and Urine Exposome: A Systematic Literature Review and Meta-Analysis

Endogenous and exogenous metabolite concentrations may be susceptible to variation over time. This variability can lead to misclassification of exposure levels and in turn to biased results. To assess the reproducibility of metabolites, the intraclass correlation coefficient (ICC) is computed. A literature search in three databases from 2000 to May 2021 was conducted to identify studies reporting ICCs for blood and urine metabolites. This review includes 192 studies, of which 31 studies are included in the meta-analyses. The ICCs of 359 single metabolites are reported, and the ICCs of 10 metabolites were meta-analyzed. The reproducibility of the single metabolites ranges from poor to excellent and is highly compound-dependent. The reproducibility of bisphenol A (BPA), mono-ethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-2-ethylhexyl phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-benzyl phthalate (MBzP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), methylparaben, and propylparaben is poor to moderate (ICC median: 0.32; range: 0.15-0.49), and for 25-hydroxyvitamin D [25(OH)D], it is excellent (ICC: 0.95; 95% CI, 0.90-0.99). Pharmacokinetics, mainly the half-life of elimination and exposure patterns, can explain reproducibility. This review describes the reproducibility of the blood and urine exposome, provides a vast dataset of ICC estimates, and hence constitutes a valuable resource for future reproducibility and clinical epidemiologic studies.

Experience of clinical services shapes attitudes to mental health data sharing: findings from a UK-wide survey

BACKGROUND

Routinely-collected mental health data could deliver novel insights for mental health research. However, patients' willingness to share their mental health data remains largely unknown. We investigated factors influencing likelihood of sharing these data for research purposes amongst people with and without experience of mental illness.

METHODS

We collected responses from a diverse sample of UK National Health Service (NHS) users (n = 2187) of which about half (n = 1087) had lifetime experience of mental illness. Ordinal logistic regression was used to examine the influence of demographic factors, clinical service experience, and primary mental illness on willingness to share mental health data, contrasted against physical health data.

RESULTS

There was a high level of willingness to share mental (89.7%) and physical (92.8%) health data for research purposes. Higher levels of satisfaction with the NHS were associated with greater willingness to share mental health data. Furthermore, people with personal experience of mental illness were more willing than those without to share mental health data, once the variable of NHS satisfaction had been controlled for. Of the mental illnesses recorded, people with depression, obsessive-compulsive disorder (OCD), personality disorder or bipolar disorder were significantly more likely to share their mental health data than people without mental illness.

CONCLUSIONS

These findings suggest that positive experiences of health services and personal experience of mental illness are associated with greater willingness to share mental health data. NHS satisfaction is a potentially modifiable factor that could foster public support for increased use of NHS mental health data in research.

Cognitive Dispersion Predicts Grip Strength Trajectories in Men but not Women in a Sample of the Oldest Old Without Dementia

Background and Objectives

Grip strength is a reliable marker of biological vitality and it typically demonstrates an expected decline in older adults. According to the common-cause hypothesis, there is also a significant association between cognitive and physical function in older adults. Some specific cognitive functions have been shown to be associated with grip strength trajectories with most research solely focused on cutoff points or mean cognitive performance. In the present study, we examine whether a measure of cognitive dispersion might be more informative. We therefore used an index that quantifies dispersion in cognitive scores across multiple cognitive tests, shown to be associated with detrimental outcomes in older adults.

Research Design and Methods

Using repeated grip strength measures from men and women aged 80 and older, free of dementia in the OCTO-Twin study, we estimated aging-related grip strength trajectories. We examined the association of cognitive dispersion and mean cognitive function with grip strength level and aging-related rate of change, accounting for known risk factors.

Results

Cognitive dispersion was associated with grip strength trajectories in men and the association varied by mean cognitive performance, whereas we found no association in women.

Discussion and Implications

Our results provide evidence of a sex-specific vitality association between cognitive dispersion and aging-related trajectories of grip strength. Our results support the call for integration of sex and gender in health promotion and intervention research.

Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease

Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.

Do I lose cognitive function as fast as my twin partner? Analyses based on classes of MMSE trajectories of twins aged 80 and older

BACKGROUND

Aging is associated with an increasing risk of decline in cognitive abilities. The decline is, however, not a homogeneous process. There are substantial differences across individuals although previous investigations have identified individuals with distinct cognitive trajectories. Evidence is accumulating that lifestyle contributes significantly to the classification of individuals into various clusters. How and whether genetically related individuals, like twins, change in a more similar manner is yet not fully understood.

METHODS

In this study, we fitted growth mixture models to Mini Mental State Exam (MMSE) scores from participants of the Swedish OCTO twin study of oldest-old monozygotic and same-sex dizygotic twins with the purpose of investigating whether twin pairs can be assigned to the same class of cognitive change.

RESULTS

We identified four distinct groups (latent classes) whose MMSE trajectories followed different patterns of change over time: two classes of high performing individuals who remained stable and declined slowly, respectively, a group of mildly impaired individuals with a fast decline and a small group of impaired individuals who declined more rapidly. Notably, our analyses show no association between zygosity and class assignment.

CONCLUSIONS

Our study provides evidence for a more substantial impact of environmental, rather than genetic, influences on cognitive change trajectories in later life.

Cognitive dispersion and ApoEe4 genotype predict dementia diagnosis in 8-year follow-up of the oldest-old

BACKGROUND

Cognitive dispersion, or inconsistencies in performance across cognitive domains, has been posited as a cost-effective tool to predict conversion to dementia in older adults. However, there is a dearth of studies exploring cognitive dispersion in the oldest-old (>80 years) and its relationship to dementia incidence.

OBJECTIVE

The main aim of this study was to examine whether higher cognitive dispersion at baseline was associated with dementia incidence within an 8-year follow-up of very old adults, while controlling for established risk factors and suggested protective factors for dementia.

METHODS

Participants (n = 468) were from the Origins of Variance in the Old-Old: Octogenarian Twins study, based on the Swedish Twin Registry. Cox regression analyses were performed to assess the association between baseline cognitive dispersion scores and dementia incidence, while controlling for sociodemographic variables, ApoEe4 carrier status, co-morbidities, zygosity and lifestyle engagement scores. An additional model included a composite of average cognitive performance.

RESULTS

Cognitive dispersion and ApoEe4 were significantly associated with dementia diagnosis. These variables remained statistically significant when global cognitive performance was entered into the model. Likelihood ratio tests revealed that cognitive dispersion and cognitive composite scores entered together in the same model was superior to either predictor alone in the full model.

CONCLUSIONS

The study underscores the usefulness of cognitive dispersion metrics for dementia prediction in the oldest-old and highlights the influence of ApoEe4 on cognition in very late age. Our findings concur with others suggesting that health and lifestyle factors pose little impact upon cognition in very advanced age.

Comparison of Cox proportional hazards regression and generalized Cox regression models applied in dementia risk prediction

INTRODUCTION

The frequently used Cox regression applies two critical assumptions, which might not hold for all predictors. In this study, the results from a Cox regression model (CM) and a generalized Cox regression model (GCM) are compared.

METHODS

Data are from the Survey of Health, Ageing and Retirement in Europe (SHARE), which includes approximately 140,000 individuals aged 50 or older followed over seven waves. CMs and GCMs are used to estimate dementia risk. The results are internally and externally validated.

RESULTS

None of the predictors included in the analyses fulfilled the assumptions of Cox regression. Both models predict dementia moderately well (10-year risk: 0.737; 95% confidence interval [CI]: 0.699, 0.773; CM and 0.746; 95% CI: 0.710, 0.785; GCM).

DISCUSSION

The GCM performs significantly better than the CM when comparing pseudo-R2 and the log-likelihood. GCMs enable researcher to test the assumptions used by Cox regression independently and relax these assumptions if necessary.

Statistical methods for dementia risk prediction and recommendations for future work: A systematic review

Introduction

Numerous dementia risk prediction models have been developed in the past decade. However, methodological limitations of the analytical tools used may hamper their ability to generate reliable dementia risk scores. We aim to review the used methodologies.

Methods

We systematically reviewed the literature from March 2014 to September 2018 for publications presenting a dementia risk prediction model. We critically discuss the analytical techniques used in the literature.

Results

In total 137 publications were included in the qualitative synthesis. Three techniques were identified as the most commonly used methodologies: machine learning, logistic regression, and Cox regression.

Discussion

We identified three major methodological weaknesses: (1) over-reliance on one data source, (2) poor verification of statistical assumptions of Cox and logistic regression, and (3) lack of validation. The use of larger and more diverse data sets is recommended. Assumptions should be tested thoroughly, and actions should be taken if deviations are detected.