Age at cardiovascular disease onset, dementia risk, and the role of lifestyle factors.

INTRODUCTION: We first examined the role of age at cardiovascular disease (CVD) onset for incident dementia, and then examined whether lifestyle factors at guideline-recommended levels in individuals with CVD mitigates dementia risk. METHODS: We used population-based data (Whitehall II: n = 10,308/baseline 1985-1988/examinations every 4-5 years). Lifestyle factors (non-smoking, body mass index [BMI], physical activity, diet) were extracted post-CVD. RESULTS: Over a median of 31.6 years, 3275 (32.1%) developed CVD. At age 70, risk of dementia was higher in individuals with CVD onset before (hazard ratio [HR] of incident dementia for participants with CVD before age 60, using participants without CVD at age 70 as the reference: 1.56, 95% confidence interal [CI] 1.18-2.08) but not after 60 years. In participants with CVD, a greater number of lifestyle factors at recommended levels post-CVD was associated with a lower dementia risk (per lifestyle factor at recommended level HR: 0.73, 95% CI 0.59-0.92). DISCUSSION: Our results suggest that early onset CVD is associated with a higher dementia risk at older ages. In those with CVD, the dementia risk was lower if lifestyle factors are at recommended levels following CVD diagnosis. HIGHLIGHTS: CVD in midlife but not in late life is associated with a higher risk of dementia. Dementia risk in CVD patients is lower if their lifestyle factors are at recommended levels. These findings provide evidence to promote CVD prevention in midlife or earlier. Study findings also show the importance of a healthy lifestyle in those with CVD.

Risk of Macrovascular and Microvascular Disease in Diabetes Diagnosed Using Oral Glucose Tolerance Test With and Without Confirmation by Hemoglobin A1c: The Whitehall II Cohort Study.

BACKGROUND: It is unclear whether replacing oral glucose tolerance test (OGTT) with hemoglobin A1c (HbA1c) measurement for diagnosing diabetes is justified. We aimed to assess the proportion of OGTT-diagnosed diabetes cases that can be confirmed by HbA1c and to examine whether individuals with OGTT diagnosis but nondiagnostic HbA1c are at higher risk of macrovascular and microvascular disease. METHODS: Participants were 5773 men and women from the population-based Whitehall II prospective cohort study in the United Kingdom. New OGTT diabetes cases diagnosed in clinical examinations in 2002 to 2004 and 2007 to 2009 were assessed for HbA1c confirmation (≥6.5%) in these and subsequent clinical examinations in 2012 to 2013 and 2015 to 2016. All participants were followed up for major cardiovascular events through linkage to electronic health records until 2017 and for incident chronic kidney disease (estimated glomerular filtration rate <60 mL·min-1·1.73 m-2) until the last clinical examination. In analysis of vascular disease risk, new OGTT-diagnosed diabetes cases with and without diagnostic HbA1c and preexisting diabetes cases were compared with diabetes-free participants. RESULTS: Of the 378 (59.3%) participants with OGTT-diagnosed diabetes, 224 were confirmed by HbA1c during 4.1 years (SD, 4.1 years) of follow-up. We recorded 942 cardiovascular events over 12.1 years. After adjustment for nonmodifiable risk factors and compared with the 4997 diabetes-free participants, 371 participants with new HbA1c-confirmed diabetes and 405 participants with preexisting diabetes had increased risk of cardiovascular disease (hazard ratio, 1.53 [95% CI, 1.12-2.10] and 1.85 [95% CI, 1.50-2.28], respectively). The corresponding hazard ratios in the analysis of incident chronic kidney disease (487 cases; follow-up, 6.6 years) were 1.69 (95% CI, 1.09-2.62) for 282 participants with new HbA1c-confirmed diabetes and 1.67 (95% CI, 1.22-2.28) for 276 participants with preexisting diabetes. In both analyses, OGTT cases with nondiagnostic HbA1c (n=149 and 107) had a risk (hazard ratio, 0.99-1.07) similar to that of the diabetes-free population. CONCLUSIONS: More than 40% of OGTT-diagnosed diabetes cases were not confirmed by HbA1c during an extended follow-up. However, because these individuals have a risk of cardiovascular disease and chronic kidney disease similar to that of the diabetes-free population, replacement of OGTT with HbA1c-based diagnosis appears justified.

Is metabolic-healthy obesity associated with risk of dementia? An age-stratified analysis of the Whitehall II cohort study.

BACKGROUND: Metabolically healthy obesity is hypothesized to be a benign condition but whether this is the case for dementia remains debated. We examined the role of age at assessment of metabolic-obesity phenotypes in associations with incident dementia. METHODS: Obesity (body mass index ≥ 30 kg/m2) and poor metabolic health (≥ 2 of elevated serum triglycerides, low HDL-C, elevated blood pressure, and elevated serum fasting glucose) were used to define four metabolic-obesity phenotypes (metabolically healthy (MHNO) and unhealthy non-obesity (MUNO), metabolically healthy (MHO) and unhealthy obesity (MUO)) at < 60, 60 to < 70, and ≥ 70 years using 6 waves of data from the Whitehall II study and their associations with incident dementia was examined using Cox regression. RESULTS: Analyses with exposures measured < 60, 60 to < 70, and ≥ 70 years involved 410 (5.8%), 379 (5.6%), and 262 (7.4%) incident dementia cases over a median follow-up of 20.8, 10.3, and 4.2 years respectively. In analyses of individual components, obesity before 60 years (HR 1.41, 95% CI: [1.08, 1.85]) but not at older ages was associated with dementia; unhealthy metabolic status when present < 60 years (HR 1.33, 95% CI: [1.08, 1.62]) and 60 to < 70 years (HR 1.32, 95% CI: [1.07, 1.62]) was associated with dementia. Compared to the metabolically healthy non-obesity group, the risk of dementia was higher in those with metabolically healthy obesity before 60 years (1.69; 95% CI: [1.16, 2.45]); this was not the case when metabolic-obesity phenotype was present at 60 to < 70 years or ≥ 70 years. Analyses at older ages were on smaller numbers due to death and drop-out but inverse probability weighting to account for missing data yielded similar results. CONCLUSIONS: Individuals with metabolically healthy obesity before age 60 had a higher risk of incident dementia over a 27-year follow-up; the excess risk dissipates when metabolic health and obesity are measured after 70 years.

Trajectories of cardiac troponin in the decades before cardiovascular death: a longitudinal cohort study.

BACKGROUND: High-sensitivity cardiac troponin testing is a promising tool for cardiovascular risk prediction, but whether serial testing can dynamically predict risk is uncertain. We evaluated the trajectory of cardiac troponin I in the years prior to a cardiovascular event in the general population, and determine whether serial measurements could track risk within individuals. METHODS: In the Whitehall II cohort, high-sensitivity cardiac troponin I concentrations were measured on three occasions over a 15-year period. Time trajectories of troponin were constructed in those who died from cardiovascular disease compared to those who survived or died from other causes during follow up and these were externally validated in the HUNT Study. A joint model that adjusts for cardiovascular risk factors was used to estimate risk of cardiovascular death using serial troponin measurements. RESULTS: In 7,293 individuals (mean 58 ± 7 years, 29.4% women) cardiovascular and non-cardiovascular death occurred in 281 (3.9%) and 914 (12.5%) individuals (median follow-up 21.4 years), respectively. Troponin concentrations increased in those dying from cardiovascular disease with a steeper trajectory compared to those surviving or dying from other causes in Whitehall and HUNT (Pinteraction < 0.05 for both). The joint model demonstrated an independent association between temporal evolution of troponin and risk of cardiovascular death (HR per doubling, 1.45, 95% CI,1.33-1.75). CONCLUSIONS: Cardiac troponin I concentrations increased in those dying from cardiovascular disease compared to those surviving or dying from other causes over the preceding decades. Serial cardiac troponin testing in the general population has potential to track future cardiovascular risk.

Comparison of sex differences in cognitive function in older adults between high- and middle-income countries and the role of education: a population-based multicohort study.

BACKGROUND: The extent to which education explains variations in sex differences in cognitive function between countries at different levels of economic development is unknown. We examined the role of education in sex differences in four cognitive domains in high- and middle-income countries. METHODS: Analyses were based on 70,846 participants, aged 60 years and older, in cohort studies from a high-income (United States) and four middle-income countries (Mexico, Brazil, China, and India). We used weighted linear models to allow nationally-representative comparisons of sex differences in orientation, memory, attention, and fluency using the United States as the reference, before and after adjustment for education, and after stratification by education. RESULTS: Females had lower levels of education than males in all countries, particularly in India. Before adjustment for education, sex differences in orientation and attention in all middle-income countries, memory in Brazil, China, and India, and fluency in India were less favourable to females than in the United States (P < 0.010). For example, females outperformed males in memory in the United States (mean difference [male-female scores] = -0.26 standard deviations [95% CI -0.30, -0.22]) but not in China (0.15 [0.09, 0.21]) or India (0.16 [0.13, 0.19]). Adjustment for education attenuated these sex differences. In analyses stratified by education, there were minimal sex differences in the high education group in all countries. CONCLUSION: Education contributes to larger female disadvantages in cognitive function at older ages in middle-income countries compared with the United States. Gender equity in education is an important target to reduce sex disparities in cognitive function globally.

Change in lipids before onset of dementia, coronary heart disease, and mortality: A 28-year follow-up Whitehall II prospective cohort study.

INTRODUCTION: The association of lipids with dementia remains a subject of debate. Using data from 7,672 participants of the Whitehall II prospective cohort study, we examined whether timing of exposure, length of follow-up, or sex modifies this association. METHODS: Twelve markers of lipid levels were measured from fasting blood and eight among them a further five times. We performed time-to-event as well as trajectory analyses. RESULTS: No associations were observed in men; in women most lipids were associated with the risk of dementia, but only for events occurring after the first 20 years of follow-up. Differences in lipid trajectories in men emerged only in the years immediately before diagnosis whereas in women total cholesterol (TC), LDL-cholesterol (LDL-C), non-HDL-cholesterol (non-HDL-C), TC/HDL-C, and LDL-C/HDL-C were higher in midlife among dementia cases before declining progressively. DISCUSSION: Abnormal lipid levels in midlife seem to be associated with a higher risk of dementia in women.

Inter- and intra-individual variation in brain structural-cognition relationships in aging.

The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ±4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure.

Association of APOE ε4 with cerebral gray matter volumes in non-demented older adults: The MEMENTO cohort study.

Data on 2,045 non-demented individuals with memory complaints were drawn from the Memento cohort study to examine the association between Apolipoprotein E ε4 allele (APOE4) and regional brain gray matter volumes. Linear regression was used to examine the association of APOE4 and measures of regional gray matter volumes in cross-sectional analysis and change therein using longitudinal analyses based on two brain MRI performed at baseline and at two-year follow-up. Overall, in analyses adjusted for age, sex, and intracranial volume, the presence of APOE4 was associated with lower total gray matter volume at baseline and with a higher atrophy rate over the follow-up. The hippocampus and entorhinal cortex were the two gray matter regions most associated with APOE4. Further adjustment for cardiovascular risk factors had little impact on these associations. There was an interaction between age, APOE4 status and total brain volume atrophy rate, with evidence of an earlier age at onset of atrophy in hippocampal volume in APOE4 carriers compared to non-carriers. Those results are in accordance with the role of medial temporal structures in the greater risk of dementia observed in people carrying the APOE4 allele.

Association of sleep duration at age 50, 60, and 70 years with risk of multimorbidity in the UK: 25-year follow-up of the Whitehall II cohort study.

BACKGROUND: Sleep duration has been shown to be associated with individual chronic diseases but its association with multimorbidity, common in older adults, remains poorly understood. We examined whether sleep duration is associated with incidence of a first chronic disease, subsequent multimorbidity and mortality using data spanning 25 years. METHODS AND FINDINGS: Data were drawn from the prospective Whitehall II cohort study, established in 1985 on 10,308 persons employed in the London offices of the British civil service. Self-reported sleep duration was measured 6 times between 1985 and 2016, and data on sleep duration was extracted at age 50 (mean age (standard deviation) = 50.6 (2.6)), 60 (60.3 (2.2)), and 70 (69.2 (1.9)). Incidence of multimorbidity was defined as having 2 or more of 13 chronic diseases, follow-up up to March 2019. Cox regression, separate analyses at each age, was used to examine associations of sleep duration at age 50, 60, and 70 with incident multimorbidity. Multistate models were used to examine the association of sleep duration at age 50 with onset of a first chronic disease, progression to incident multimorbidity, and death. Analyses were adjusted for sociodemographic, behavioral, and health-related factors. A total of 7,864 (32.5% women) participants free of multimorbidity had data on sleep duration at age 50; 544 (6.9%) reported sleeping ≤5 hours, 2,562 (32.6%) 6 hours, 3,589 (45.6%) 7 hours, 1,092 (13.9%) 8 hours, and 77 (1.0%) ≥9 hours. Compared to 7-hour sleep, sleep duration ≤5 hours was associated with higher multimorbidity risk (hazard ratio: 1.30, 95% confidence interval = 1.12 to 1.50; p < 0.001). This was also the case for short sleep duration at age 60 (1.32, 1.13 to 1.55; p < 0.001) and 70 (1.40, 1.16 to 1.68; p < 0.001). Sleep duration ≥9 hours at age 60 (1.54, 1.15 to 2.06; p = 0.003) and 70 (1.51, 1.10 to 2.08; p = 0.01) but not 50 (1.39, 0.98 to 1.96; p = 0.07) was associated with incident multimorbidity. Among 7,217 participants free of chronic disease at age 50 (mean follow-up = 25.2 years), 4,446 developed a first chronic disease, 2,297 progressed to multimorbidity, and 787 subsequently died. Compared to 7-hour sleep, sleeping ≤5 hours at age 50 was associated with an increased risk of a first chronic disease (1.20, 1.06 to 1.35; p = 0.003) and, among those who developed a first disease, with subsequent multimorbidity (1.21, 1.03 to 1.42; p = 0.02). Sleep duration ≥9 hours was not associated with these transitions. No association was found between sleep duration and mortality among those with existing chronic diseases. The study limitations include the small number of cases in the long sleep category, not allowing conclusions to be drawn for this category, the self-reported nature of sleep data, the potential for reverse causality that could arise from undiagnosed conditions at sleep measures, and the small proportion of non-white participants, limiting generalization of findings. CONCLUSIONS: In this study, we observed short sleep duration to be associated with risk of chronic disease and subsequent multimorbidity but not with progression to death. There was no robust evidence of an increased risk of chronic disease among those with long sleep duration at age 50. Our findings suggest an association between short sleep duration and multimorbidity.

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study.

BACKGROUND: Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age. METHODS: Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R2, Akaike's information criterion, sensitivity, specificity, C-statistic and calibration. RESULTS: Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05. CONCLUSIONS: Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.

Association between kidney function and incidence of dementia: 10-year follow-up of the Whitehall II cohort study.

BACKGROUND: Cognitive dysfunction is common in haemodialysis patients but whether poor kidney function in the general population is also associated with higher risk of dementia remains unclear. OBJECTIVE: To examine the association of kidney function with incident dementia in community dwelling older adults. DESIGN: Whitehall II prospective study. SETTING: Population-based study on 6,050 adults, mean age 65.8 in 2007-2009. METHODS: Poor kidney function, defined as estimated Glomerular Filtration Rate (eGFR) <60 ml/min/1.73 m2 in 2007-2009, and adverse change in eGFR was defined as decrease ≥4 ml/min/1.73 m2 between 2007-2009 and 2012-2013.Incident dementia was ascertained through linkage to electronic health records, and Cox regression was used to examine associations with dementia. RESULTS: A total of 306 cases of dementia were recorded over a mean follow-up of 10 years. Baseline eGFR <60 was associated with a hazard ratio (HR) for dementia of 1.37 (95% CI 1.02, 1.85) in analysis adjusted for sociodemographic factors, hypertension, obesity, stroke, diabetes and cardiovascular disease/medication. Removing stroke cases at baseline and censoring them over the follow-up yielded an HR of 1.42 (95% CI 1.00, 2.00) for the association between CKD and dementia. Decline of eGFR ≥4 between 2007-2009 and 2012-2013 was associated with incidence of dementia over a 6.3 year mean follow-up (HR: 1.37; 95% CI 1.02, 1.85), with somewhat stronger associations when analyses were restricted to those with eGFR ≥60 in 2007-2009 (1.56; 95% CI: 1.12, 2.19). CONCLUSION: Poor and declining kidney function in older adults is associated with a higher risk of dementia that is not attributable to stroke and persists after accounting for major cardiometabolic conditions.

Plasma proteins, cognitive decline, and 20-year risk of dementia in the Whitehall II and Atherosclerosis Risk in Communities studies.

INTRODUCTION: Plasma proteins affect biological processes and are common drug targets but their role in the development of Alzheimer's disease and related dementias remains unclear. We examined associations between 4953 plasma proteins and cognitive decline and risk of dementia in two cohort studies with 20-year follow-ups. METHODS: In the Whitehall II prospective cohort study proteins were measured using SOMAscan technology. Cognitive performance was tested five times over 20 years. Linkage to electronic health records identified incident dementia. The results were replicated in the Atherosclerosis Risk in Communities (ARIC) study. RESULTS: Fifteen non-amyloid/non-tau-related proteins were associated with cognitive decline and dementia, were consistently identified in both cohorts, and were not explained by known dementia risk factors. Levels of six of the proteins are modifiable by currently approved medications for other conditions. DISCUSSION: This study identified several plasma proteins in dementia-free people that are associated with long-term risk of cognitive decline and dementia.

Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.

Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging.

The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no "gold standard" for measuring these constructs. Using machine-learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34-82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.

Appetite disinhibition rather than hunger explains genetic effects on adult BMI trajectory.

BACKGROUND/OBJECTIVES: The mediating role of eating behaviors in genetic susceptibility to weight gain during mid-adult life is not fully understood. This longitudinal study aims to help us understand contributions of genetic susceptibility and appetite to weight gain. SUBJECTS/METHODS: We followed the body-mass index (BMI) trajectories of 2464 adults from 45 to 65 years of age by measuring weight and height on four occasions at 5-year intervals. Genetic risk of obesity (gene risk score: GRS) was ascertained, comprising 92 BMI-associated single-nucleotide polymorphisms and split at a median (=high and low risk). At the baseline, the Eating Inventory was used to assess appetite-related traits of 'disinhibition', indicative of opportunistic eating or overeating and 'hunger' which is susceptibility to/ability to cope with the sensation of hunger. Roles of the GRS and two appetite-related scores for BMI trajectories were examined using a mixed model adjusted for the cohort effect and sex. RESULTS: Disinhibition was associated with higher BMI (ß = 2.96; 95% CI: 2.66-3.25 kg/m2), and accounted for 34% of the genetically-linked BMI difference at age 45. Hunger was also associated with higher BMI (ß = 1.20; 0.82-1.59 kg/m2) during mid-life and slightly steeper weight gain, but did not attenuate the effect of disinhibition. CONCLUSIONS: Appetite disinhibition is most likely to be a defining characteristic of genetic susceptibility to obesity. High levels of appetite disinhibition, rather than hunger, may underlie genetic vulnerability to obesogenic environments in two-thirds of the population of European ancestry.

Subjective Cognitive Complaints Given in Questionnaire: Relationship With Brain Structure, Cognitive Performance and Self-Reported Depressive Symptoms in a 25-Year Retrospective Cohort Study.

BACKGROUND: Subjective cognitive complaints are common but it is unclear whether they indicate an underlying pathological process or reflect affective symptoms. METHOD: 800 community-dwelling older adults were drawn from the Whitehall II cohort. Subjective cognitive complaint inquiry for memory and concentration, a range of neuropsychological tests and multimodal MRI were performed in 2012-2016. Subjective complaints were again elicited after 1 year. Group differences in grey and white matter, between those with and without subjective complaints, were assessed using voxel-based morphometry and tract-based spatial statistics, respectively. Mixed effects models assessed whether cognitive decline or depressive symptoms (over a 25-year period) were associated with later subjective complaints. Analyses were controlled for potential confounders and multiple comparisons. RESULTS: Mean age of the sample at scanning was 69.8 years (±5.1, range: 60.3-84.6). Subjective memory complaints were common (41%) and predicted further similar complaints later (mean 1.4 ± 1.4 years). There were no group differences in grey matter density or white matter integrity. Subjective complaints were not cross-sectionally or longitudinally associated with objectively assessed cognition. However, those with subjective complaints reported higher depressive symptoms ("poor concentration": odds ratio = 1.12, 95% CI 1.07-1.18; "poor memory": odds ratio = 1.18, 1.12-1.24). CONCLUSIONS: In our sample subjective complaints were consistent over time and reflected depressive symptoms but not markers of neurodegenerative brain damage or concurrent or future objective cognitive impairment. Clinicians assessing patients presenting with memory complaints should be vigilant for affective disorders. These results question the rationale for including subjective complaints in a spectrum with Mild Cognitive Impairment diagnostic criteria.

Serum transthyretin and risk of cognitive decline and dementia: 22-year longitudinal study.

Serum transthyretin (TTR) may be an early biomarker for Alzheimer's disease and related disorders (ADRD). We investigated associations of TTR measured at baseline with cognitive decline and incident ADRD and whether TTR trajectories differ between ADRD cases and non-cases, over 22 years before diagnosis. A total of 6024 adults aged 45-69 in 1997-1999 were followed up until 2019. TTR was assessed three times, and 297 cases of dementia were recorded. Higher TTR was associated with higher cognitive function at baseline; however, TTR was unrelated to subsequent change in cognitive function. TTR at baseline did not predict ADRD risk (hazard ratio per SD TTR (4.8 mg/dL) = 0.97; 95% confidence interval: 0.94-1.00). Among those later diagnosed with ADRD, there was a marginally steeper downward TTR trajectory than those free of ADRD over follow-up (P=0.050). Our findings suggest TTR is not neuroprotective. The relative decline in TTR level in the preclinical stage of ADRD is likely to be a consequence of disease processes.

Association Between Age at Diabetes Onset and Subsequent Risk of Dementia.

Importance: Trends in type 2 diabetes show an increase in prevalence along with younger age of onset. While vascular complications of early-onset type 2 diabetes are known, the associations with dementia remains unclear. Objective: To determine whether younger age at diabetes onset is more strongly associated with incidence of dementia. Design, Setting, and Participants: Population-based study in the UK, the Whitehall II prospective cohort study, established in 1985-1988, with clinical examinations in 1991-1993, 1997-1999, 2002-2004, 2007-2009, 2012-2013, and 2015-2016, and linkage to electronic health records until March 2019. The date of final follow-up was March 31, 2019. Exposures: Type 2 diabetes, defined as a fasting blood glucose level greater than or equal to 126 mg/dL at clinical examination, physician-diagnosed type 2 diabetes, use of diabetes medication, or hospital record of diabetes between 1985 and 2019. Main Outcomes and Measures: Incident dementia ascertained through linkage to electronic health records. Results: Among 10 095 participants (67.3% men; aged 35-55 years in 1985-1988), a total of 1710 cases of diabetes and 639 cases of dementia were recorded over a median follow-up of 31.7 years. Dementia rates per 1000 person-years were 8.9 in participants without diabetes at age 70 years, and rates were 10.0 per 1000 person-years for participants with diabetes onset up to 5 years earlier, 13.0 for 6 to 10 years earlier, and 18.3 for more than 10 years earlier. In multivariable-adjusted analyses, compared with participants without diabetes at age 70, the hazard ratio (HR) of dementia in participants with diabetes onset more than 10 years earlier was 2.12 (95% CI, 1.50-3.00), 1.49 (95% CI, 0.95-2.32) for diabetes onset 6 to 10 years earlier, and 1.11 (95% CI, 0.70-1.76) for diabetes onset 5 years earlier or less; linear trend test (P < .001) indicated a graded association between age at onset of type 2 diabetes and dementia. At age 70, every 5-year younger age at onset of type 2 diabetes was significantly associated with an HR of dementia of 1.24 (95% CI, 1.06-1.46) in analyses adjusted for sociodemographic factors, health behaviors, and health-related measures. Conclusions and Relevance: In this longitudinal cohort study with a median follow-up of 31.7 years, younger age at onset of diabetes was significantly associated with higher risk of subsequent dementia.

Association of moderate and vigorous physical activity with incidence of type 2 diabetes and subsequent mortality: 27 year follow-up of the Whitehall II study.

AIMS/HYPOTHESIS: This work examined the role of physical activity in the course of diabetes using data spanning nearly three decades. Our first aim was to examine the long-term association of moderate and vigorous physical activity with incidence of type 2 diabetes. Our second aim was to investigate the association of moderate-to-vigorous physical activity post-diabetes diagnosis with subsequent risk of all-cause and cardiovascular disease mortality. METHODS: A total of 9987 participants from the Whitehall II cohort study free of type 2 diabetes at baseline (1985-1988) were followed for incidence of type 2 diabetes, based on clinical assessments between 1985 and 2016 and linkage to electronic health records up to 31 March 2017. We first examined the association of moderate and vigorous physical activity measured by questionnaire in 1985-1988 (mean age 44.9 [SD 6.0] years; women, 32.7%) with incident type 2 diabetes, using the interval-censored, illness-death model, a competing risk analysis that takes into account both competing risk of death and intermittent ascertainment of diabetes due to reliance on data collection cycles (interval-censored). The second analysis was based on individuals with type 2 diabetes over the follow-up period where we used Cox regression with inverse probability weighting to examine the association of moderate-to-vigorous physical activity after diagnosis of type 2 diabetes with risk of all-cause and cardiovascular disease mortality. RESULTS: Of the 9987 participants, 1553 developed type 2 diabetes during a mean follow-up of 27.1 (SD 6.3) years. Compared with participants who were inactive in 1985-1988, those who undertook any duration of moderate-to-vigorous physical activity had a lower risk of type 2 diabetes (HR 0.85 [95% CI 0.75, 0.97], p = 0.02; analysis adjusted for sociodemographic, behavioural and health-related factors). In 1026 participants with a diagnosis of type 2 diabetes over the follow-up period, data on moderate-to-vigorous physical activity after diabetes diagnosis were available; 165 all-cause deaths and 55 cardiovascular disease-related deaths were recorded during a mean follow-up of 8.8 (SD 6.1) years. In these participants with diabetes, any duration of moderate-to-vigorous physical activity was associated with lower all-cause mortality (HR 0.61 [95% CI 0.41, 0.93], p = 0.02) while the association with cardiovascular mortality was evident only for physical activity undertaken at or above recommendations (≥2.5 h per week of moderate-to-vigorous physical activity or ≥1.25 h per week of vigorous physical activity; HR 0.40 [95% CI 0.16, 0.96], p = 0.04) in fully adjusted models. CONCLUSIONS/INTERPRETATION: Moderate-to-vigorous physical activity plays an important role in diabetes, influencing both its incidence and prognosis. A protective effect on incidence was seen for durations of activity below recommendations and a marginal additional benefit was observed at higher durations. Among individuals with type 2 diabetes, any duration of moderate-to-vigorous physical activity was associated with reduced all-cause mortality while recommended durations of physical activity were required for protection against cardiovascular disease-related mortality. DATA AVAILABILITY: Whitehall II data, protocols and other metadata are available to the scientific community. Please refer to the Whitehall II data sharing policy at https://www.ucl.ac.uk/epidemiology-health-care/research/epidemiology-and-public-health/research/whitehall-ii/data-sharing.

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study.

Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.