The mediating role of comorbid conditions in the association between type 2 diabetes and cognition: a cross-sectional observational study using the UK Biobank cohort

Aims: Using the UK Biobank cohort, a large sample of middle aged and older adults in the UK, the present study aimed to examine the cross-sectional association between type 2 diabetes and cognition and to assess the hypothesised mediating role of common comorbid conditions, whilst controlling for important demographic and lifestyle factors. Methods: Using regression models and general structural equation models, we examined the cross-sectional association between type 2 diabetes status and: fluid intelligence; reaction time; visual memory; digit span and prospective memory; and the hypothesised mediating role of common comorbid conditions: visceral obesity; sleep problems; macrovascular problems; respiratory problems,; cancer and depressive symptoms in 47,468 participants from the UK Biobank cohort, of whom 1,831 have type 2 diabetes. We controlled for ethnicity, sex, age, deprivation, smoking status, alcohol consumption, physical activity levels and use of diabetes medication. Results: Participants with type 2 diabetes had a significantly shorter digit span, b = -0.14, 99.2% CIs [-0.27, -0.11] than those without type 2 diabetes. Those with type 2 diabetes did not differ from those without type 2 diabetes on fluid intelligence, reaction time, visual memory and prospective memory. The associations that do exist between type 2 diabetes and cognition are consistently mediated via macrovascular problems, depressive symptoms, and to a lesser extent visceral obesity. Respiratory problems, sleep disturbances and cancer did not mediate the association between type 2 diabetes status and measures of cognition. Conclusions: Comorbid conditions explain some of the observed association between type 2 diabetes and cognitive deficits. This suggests that prevention, management or treatment of these comorbid conditions may be important to reduce the likelihood of cognitive decline. Treatment studies with long follow-ups are needed to examine this. Tweet: Comorbid conditions explain the association between type 2 diabetes and cognitive deficits. Prevention, management or treatment of these comorbid conditions may prevent or delay the onset of cognitive decline in people with type 2 diabetes

Vitamin D Status and Depressive Symptoms in Older Adults:A Role for Physical Functioning?

Objectives: Depressive symptoms and low vitamin D status are common in older persons and may be associated, but findings are inconsistent. This study investigated whether 25-hydroxyvitamin D (25(OH)D) concentrations are associated with depressive symptoms in older adults, both cross-sectionally and longitudinally. We also examined whether physical functioning could explain this relationship, to gain a better understanding of the underlying mechanisms. Methods: Data from two independent prospective cohorts of the Longitudinal Aging Study Amsterdam were used: an older cohort (≥65 years, n = 1282, assessed from 1995–2002) and a younger-old cohort (55–65 years, n = 737, assessed from 2002–2009). Measurements: Depressive symptoms were measured at baseline and after 3 and 6 years with the Center of Epidemiological Studies Depression Scale. Cross-sectional and longitudinal linear regression techniques were used to examine the relationship between 25(OH)D and depressive symptoms. The mediating role of physical functioning was examined in the longitudinal models. Results: Cross-sectionally, associations were not significant after adjustment for confounders. Longitudinally, women in the older cohort with baseline 25(OH)D concentrations up to 75 nmol/L experienced 175 to 24% more depressive symptoms in the following 6 years, compared with women with 25(OH)D concentrations >75 nmol/L. Reduced physical performance partially mediated this relationship. In men and in the younger-old cohort, no significant associations were observed. Conclusions: Older women showed an inverse relationship between 25(OH)D and depressive symptoms over time, which may partially be explained by declining physical functioning. Replication of these findings by future studies is needed

Transitions across cognitive states and death among older adults in relation to education:A multistate survival model using data from six longitudinal studies

INTRODUCTION: This study examines the role of educational attainment, an indicator of cognitive reserve, on transitions in later life between cognitive states (normal Mini-Mental State Examination (MMSE), mild MMSE impairment, and severe MMSE impairment) and death. METHODS: Analysis of six international longitudinal studies was performed using a coordinated approach. Multistate survival models were used to estimate the transition patterns via different cognitive states. Life expectancies were estimated. RESULTS: Across most studies, a higher level of education was associated with a lower risk of transitioning from normal MMSE to mild MMSE impairment but was not associated with other transitions. Those with higher levels of education and socioeconomic status had longer nonimpaired life expectancies. DISCUSSION: This study highlights the importance of education in later life and that early life experiences can delay later compromised cognitive health. This study also demonstrates the feasibility and benefit in conducting coordinated analysis across multiple studies to validate findings

Trajectories of frailty with aging:Coordinated analysis of five longitudinal studies

BACKGROUND AND OBJECTIVES: There is an urgent need to better understand frailty and its predisposing factors. Although numerous cross-sectional studies have identified various risk and protective factors of frailty, there is a limited understanding of longitudinal frailty progression. Furthermore, discrepancies in the methodologies of these studies hamper comparability of results. Here, we use a coordinated analytical approach in 5 independent cohorts to evaluate longitudinal trajectories of frailty and the effect of 3 previously identified critical risk factors: sex, age, and education. RESEARCH DESIGN AND METHODS: We derived a frailty index (FI) for 5 cohorts based on the accumulation of deficits approach. Four linear and quadratic growth curve models were fit in each cohort independently. Models were adjusted for sex/gender, age, years of education, and a sex/gender-by-age interaction term. RESULTS: Models describing linear progression of frailty best fit the data. Annual increases in FI ranged from 0.002 in the Invecchiare in Chianti cohort to 0.009 in the Longitudinal Aging Study Amsterdam (LASA). Women had consistently higher levels of frailty than men in all cohorts, ranging from an increase in the mean FI in women from 0.014 in the Health and Retirement Study cohort to 0.046 in the LASA cohort. However, the associations between sex/gender and rate of frailty progression were mixed. There was significant heterogeneity in within-person trajectories of frailty about the mean curves. DISCUSSION AND IMPLICATIONS: Our findings of linear longitudinal increases in frailty highlight important avenues for future research. Specifically, we encourage further research to identify potential effect modifiers or groups that would benefit from targeted or personalized interventions

Causal Mediation Effects in Single Case Experimental Designs

Single case experimental designs (SCEDs) are used to test treatment effects in a wide range of fields and consist of repeated measurements for a single case throughout one or more baseline phases and throughout one or more treatment phases. Recently, mediation analysis has been applied to SCEDs. Mediation analysis decomposes the total treatment-outcome effect into a direct and indirect effect, and therefore aims to unravel the causal processes underlying treatment-outcome effects. The most recent methodological advancement for mediation analysis is the development of causal mediation analysis methodology which clarifies the necessary causal assumptions for mediation analysis. The goal of this article is to derive the causal mediation effects and corresponding standard errors based on piecewise linear regression models for the mediator and outcome and to evaluate the performance of these regression estimators and standard errors. Whereas previous studies estimated the direct and indirect effects as either the change in level or change in trend, we showed that the causal direct and indirect effects incorporate both the change in level and change in trend. Based on our simulation study we showed that for the causal indirect effects, Monte Carlo confidence intervals provided accurate (i.e., p =.05) Type I error rates and higher statistical power than normal theory confidence intervals. For the causal direct effects and total effect, normal theory confidence intervals provided accurate Type I error rates and higher statistical power than the Monte Carlo confidence intervals. Limitations and future directions are discusse

Statistical Mediation Analysis for Models with a Binary Mediator and a Binary Outcome: the Differences Between Causal and Traditional Mediation Analysis

Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An important recent methodological advancement in the mediation analysis literature is the development of the causal mediation analysis framework. Causal mediation analysis defines causal effects as the difference between two potential outcomes. These definitions can be applied to any mediation model to estimate natural direct and indirect effects, including models with binary variables and an exposure–mediator interaction. This paper aims to clarify the similarities and differences between the causal and traditional effect estimates for mediation models with a binary mediator and a binary outcome. Causal and traditional mediation analyses were applied to an empirical example to demonstrate these similarities and differences. Causal and traditional mediation analysis provided similar controlled direct effect estimates, but different estimates of the natural direct effects, natural indirect effects, and total effect. Traditional mediation analysis methods do not generalize well to mediation models with binary variables, while the natural effect definitions can be applied to any mediation model. Causal mediation analysis is therefore the preferred method for the analysis of mediation models with binary variables

Comparison of logistic-regression based methods for simple mediation analysis with a dichotomous outcome variable

BACKGROUND: Logistic regression is often used for mediation analysis with a dichotomous outcome. However, previous studies showed that the indirect effect and proportion mediated are often affected by a change of scales in logistic regression models. To circumvent this, standardization has been proposed. The aim of this study was to show the relative performance of the unstandardized and standardized estimates of the indirect effect and proportion mediated based on multiple regression, structural equation modeling, and the potential outcomes framework for mediation models with a dichotomous outcome. METHODS: We compared the performance of the effect estimates yielded by the three methods using a simulation study and two real-life data examples from an observational cohort study (n = 360). RESULTS: Lowest bias and highest efficiency were observed for the estimates from the potential outcomes framework and for the crude indirect effect ab and the proportion mediated ab/(ab + c') based on multiple regression and SEM. CONCLUSIONS: We advise the use of either the potential outcomes framework estimates or the ab estimate of the indirect effect and the ab/(ab + c') estimate of the proportion mediated based on multiple regression and SEM when mediation analysis is based on logistic regression. Standardization of the coefficients prior to estimating the indirect effect and the proportion mediated may not increase the performance of these estimates

Assessing the Robustness of Mediation Analysis Results Using Multiverse Analysis

There is an increasing awareness that replication should become common practice in empirical studies. However, study results might fail to replicate for various reasons. The robustness of published study results can be assessed using the relatively new multiverse-analysis methodology, in which the robustness of the effect estimates against data analytical decisions is assessed. However, the uptake of multiverse analysis in empirical studies remains low, which might be due to the scarcity of guidance available on performing multiverse analysis. Researchers might experience difficulties in identifying data analytical decisions and in summarizing the large number of effect estimates yielded by a multiverse analysis. These difficulties are amplified when applying multiverse analysis to assess the robustness of the effect estimates from a mediation analysis, as a mediation analysis involves more data analytical decisions than a bivariate analysis. The aim of this paper is to provide an overview and worked example of the use of multiverse analysis to assess the robustness of the effect estimates from a mediation analysis. We showed that the number of data analytical decisions in a mediation analysis is larger than in a bivariate analysis. By using a real-life data example from the Longitudinal Aging Study Amsterdam, we demonstrated the application of multiverse analysis to a mediation analysis. This included the use of specification curves to determine the impact of data analytical decisions on the magnitude and statistical significance of the direct, indirect, and total effect estimates. Although the multiverse analysis methodology is still relatively new and future research is needed to further advance this methodology, this paper shows that multiverse analysis is a useful method for the assessment of the robustness of the direct, indirect, and total effect estimates in a mediation analysis and thereby to inform replication studies

Explaining the association between frailty and mortality in older adults: The mediating role of lifestyle, social, psychological, cognitive, and physical factors

Frailty is associated with a higher risk of mortality, but not much is known about underlying pathways of the frailty-mortality association. In this study, we explore a wide range of possible mediators of the relation between frailty and mortality. Data were used from the Longitudinal Aging Study Amsterdam (LASA). We included 1477 older adults aged 65 years and over who participated in the study in 2008–2009 and linked their data to register data on mortality up to 2015. We examined a range of lifestyle, social, psychological, cognitive, and physical factors as potential mediators. All analyses were stratified by sex. We used causal mediation analyses to estimate the indirect effects in single-mediator analyses. Statistically significant mediators were then included in multiple-mediator analyses to examine their combined effect. The results showed that older men (OR = 2.79, 95% CI = 1.23;6.34) and women (OR = 2.31, 95% CI = 1.24;4.30) with frailty had higher odds of being deceased 6 years later compared to those without frailty. In men, polypharmacy (indirect effect OR = 1.21, 95% CI = 1.03;1.50) was a statistically significant mediator in this association. In women, polypharmacy, self-rated health, and multimorbidity were statistically significant mediators in the single-mediator models, but only the indirect effect of polypharmacy remained in the multiple-mediator model (OR = 1.16, 95% CI = 1.03;1.38). In conclusion, of many factors that were considered, we identified polypharmacy as explanatory factor of the association between frailty and mortality in older men and women. This finding has important clinical implications, as it suggests that targeting polypharmacy in frail older adults could reduce their risk of mortality

Noncollapsibility and its role in quantifying confounding bias in logistic regression

BACKGROUND: Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias. METHODS: A Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects. RESULTS: The simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect. CONCLUSION: In logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model