Body mass index (BMI) is associated with cognitive abilities, but the nature of the relationship remains largely unexplored. We aimed to investigate the bidirectional relationship from midlife ...through late-life, while considering sex differences and genetic predisposition to higher BMI. We used data from 23,892 individuals of European ancestry from the Health and Retirement Study, with longitudinal data on BMI and three established cognitive indices: mental status, episodic memory, and their sum, called total cognition. To investigate the dynamic relationship between BMI and cognitive abilities, we applied dual change score models of change from age 50 through 89, with a breakpoint at age 65 or 70. Models were further stratified by sex and genetic predisposition to higher BMI using tertiles of a polygenic score for BMI (PGS
). We demonstrated bidirectional effects between BMI and all three cognitive indices, with higher BMI contributing to steeper decline in cognitive abilities in both midlife and late-life, and higher cognitive abilities contributing to less decline in BMI in late-life. The effects of BMI on change in cognitive abilities were more evident in men compared to women, and among those in the lowest tertile of the PGS
compared to those in the highest tertile, while the effects of cognition on BMI were similar across groups. In conclusion, these findings highlight a reciprocal relationship between BMI and cognitive abilities, indicating that the negative effects of a higher BMI persist from midlife through late-life, and that weight-loss in late-life may be driven by cognitive decline.
While a high body mass index (BMI) in midlife is associated with higher risk of dementia, high BMI in late-life may be associated with lower risk. This study combined genetic designs with ...longitudinal data to achieve a better understanding of this paradox.
We used longitudinal data from 22,156 individuals in the Swedish Twin Registry (STR) and 25,698 from the Health and Retirement Study (HRS). The STR sample had information about BMI from early adulthood through late-life, and the HRS sample from age 50 through late-life. Survival analysis was applied to investigate age-specific associations between BMI and dementia risk. To examine if the associations are influenced by genetic susceptibility to higher BMI, an interaction between BMI and a polygenic score for BMI (PGS
) was included in the models and results stratified into those with genetic predisposition to low, medium, and higher BMI. In the STR, co-twin control models were applied to adjust for familial factors beyond those captured by the PGS
.
At age 35-49, 5 units higher BMI was associated with 15% (95% CI 7-24%) higher risk of dementia in the STR. There was a significant interaction (p = 0.04) between BMI and the PGS
, and the association present only among those with genetic predisposition to low BMI (HR 1.38, 95% CI 1.08-1.78). Co-twin control analyses indicated genetic influences. After age 80, 5 units higher BMI was associated with 10-11% lower risk of dementia in both samples. There was a significant interaction between late-life BMI and the PGS
in the STR (p = 0.01), but not the HRS, with the inverse association present only among those with a high PGS
(HR 0.70, 95% CI 0.52-0.94)
No genetic influences were evident from co-twin control models of late-life BMI.
Not only does the association between BMI and dementia differ depending on age at BMI measurement, but also the effect of genetic influences. In STR, the associations were only present among those with a BMI in opposite direction of their genetic predisposition, indicating that the association between BMI and dementia across the life course might be driven by environmental factors and hence likely modifiable.
Population-based health registers are potential assets in epidemiological research; however, the quality of case ascertainment is crucial.
To compare the case ascertainment of dementia, from the ...National Patient Register (NPR) and the Cause of Death Register (CDR) with dementia diagnoses from six Swedish population based studies.
Sensitivity, specificity, and positive predictive value (PPV) of dementia identification in NPR and CDR were estimated by individual record linkage with six Swedish population based studies (n = 19,035). Time to detection in NPR was estimated using data on dementia incidence from longitudinal studies with more than two decades of follow-up.
Barely half of the dementia cases were ever detected by NPR or CDR. Using data from longitudinal studies we estimated that a record with a dementia diagnosis appears in the NPR on average 5.5 years after first diagnosis. Although the ability of the registers to detect dementia cases was moderate, the ability to detect non-dementia cases was almost perfect (99%). When registers indicate that there is a dementia diagnosis, there are very few instances in which the clinicians determined the person was not demented. Indeed, PPVs were close to 90%. However, misclassification between dementia subtype diagnoses is quite common, especially in NPR.
Although the overall sensitivity is low, the specificity and the positive predictive value are very high. This suggests that hospital and death registers can be used to identify dementia cases in the community, but at the cost of missing a large proportion of the cases.
Frailty has been identified as a risk factor for cognitive impairment and dementia. However, it is not known whether familial factors, such as genetics and shared environmental factors, underlie this ...association. We analyzed the association between frailty and the risk of dementia in a large twin cohort and examined the role of familial factors in the association.
The Rockwood frailty index (FI) based on 44 health deficits was used to assess frailty. The population-level association between FI and the risk of all-cause dementia was analyzed in 41,550 participants of the Screening Across the Lifespan Twin (SALT) study (full sample, aged 41-97 years at baseline), using Cox and competing risk models. A subsample of 10,487 SALT participants aged 65 and older who received a cognitive assessment (cognitive sample) was used in a sensitivity analysis to assess the effect of baseline cognitive level on the FI-dementia association. To analyze the influence of familial effects on the FI-dementia association, a within-pair analysis was performed. The within-pair model was also used to assess whether the risk conferred by frailty varies by age at FI assessment.
A total of 3183 individuals were diagnosed with dementia during the 19-year follow-up. A 10% increase in FI was associated with an increased risk of dementia (hazard ratio HR 1.17 (95% confidence interval CI 1.07, 1.18)) in the full sample adjusted for age, sex, education, and tobacco use. A significant association was likewise found in the cognitive sample, with an HR of 1.13 (95% CI 1.09, 1.20), adjusted for age, sex, and cognitive level at baseline. The associations were not attenuated when adjusted for APOE ɛ4 carrier status or considering the competing risk of death. After adjusting for familial effects, we found no evidence for statistically significant attenuation of the effect. The risk conferred by higher FI on dementia was constant after age 50 until very old age.
A higher level of frailty predicts the risk of dementia and the association appears independent of familial factors. Targeting frailty might thus contribute to preventing or delaying dementia.
Differences in gene-wide DNA methylation of the Alzheimer's disease (AD)-associated genes
, and
are reported to be associated with AD in post-mortem brain samples. We investigated whether the same ...associations could be found in leukocytes collected pre-mortem. Using cohort data of 544 Swedish twins (204 dementia diagnoses), we replicated the findings in
and
at
< 0.05. However, co-twin control analyses indicated that the associations were partly explained by familial confounding. Thus, DNA methylation differences in
and
are present in both neuronal cells and leukocytes, and not fully explained familial factors.
Both educational attainment and genetic propensity to education (PGSEdu) have been associated with geographic mobility. Socioeconomic conditions are, in turn, associated with individuals’ health. ...Geographic mobility could therefore lead to better health for some since it could provide better opportunities, like education. Our aim was to study how attained education and genetic predisposition for higher education are related to geographic mobility, and how they affect the association between geographic mobility and mortality.
We used data from the Swedish Twin Registry (twins born 1926–1955; n = 14,211) in logistic regression models to test if attained education and PGSEdu predicted geographic mobility. Cox regression models were then performed to test if geographic mobility, attained education, and PGSEdu were associated with mortality.
The results show that both attained education and PGSEdu predicted geographic mobility, in both independent and joint effect models, with higher education associated with higher mobility. Geographic mobility was associated with lower mortality in the independent effect model, but joint effect models showed that this association was completely explained by attained education.
To conclude, both attained education and PGSEdu were associated with geographic mobility. Moreover, attained education explained the relationship between geographic mobility and mortality.
•Genetic propensity to education and attained education predicted geographic mobility.•Attained education explains the relation between geographic mobility and mortality.•The association between education and mortality was not explained by genetic factors.
Metabolically healthy obesity may be a transient phenotype, but studies with long follow-up, especially covering late-life, are lacking. We describe conversions between cross-categories of body mass ...index (BMI) and metabolic health in 786 Swedish twins with up to 27 years of follow-up, from midlife to late-life.
Metabolic health was defined as the absence of metabolic syndrome (MetS). We first visualized conversions between BMI-metabolic health phenotypes in 100 individuals with measurements available at ages 50-64, 65-79, and ≥80. Next, we modeled conversion in metabolic health status by BMI category in the full sample using Cox proportional hazards regression.
The proportion of individuals with MetS and with overweight or obesity increased with age. However, one-fifth maintained a metabolically healthy overweight or obesity across all three age categories. Among those metabolically healthy at baseline, 59% converted to MetS during follow-up. Conversions occurred 56% more often among individuals with metabolically healthy obesity, but not overweight, compared to normal weight. Among those with MetS at baseline, 60% regained metabolic health during follow-up, with no difference between BMI categories.
Conversions between metabolically healthy and unhealthy status occurred in both directions in all BMI categories. While conversions to MetS were more common among individuals with obesity, many individuals maintained or regained metabolic health during follow-up.
There is robust evidence that in midlife, higher body mass index (BMI) and metabolic syndrome (MetS), which often co-exist, are associated with increased mortality risk. However, late-life findings ...are inconclusive, and few studies have examined how metabolic health status (MHS) affects the BMI-mortality association in different age categories. We, therefore, aimed to investigate how mid- and late-life BMI and MHS interact to affect the risk of mortality.
This cohort study included 12,467 participants from the Swedish Twin Registry, with height, weight, and MHS measures from 1958-2008 and mortality data linked through 2020. We applied Cox proportional hazard regression with age as a timescale to examine how BMI categories (normal weight, overweight, obesity) and MHS (identification of MetS determined by presence/absence of hypertension, hyperglycemia, low HDL, hypertriglyceridemia), independently and in interaction, are associated with the risk of all-cause mortality. Models were adjusted for sex, education, smoking, and cardiovascular disease.
The midlife group included 6,252 participants with a mean age of 59.6 years (range = 44.9-65.0) and 44.1% women. The late-life group included 6,215 participants with mean age 73.1 years (65.1-95.3) and 46.6% women. In independent effect models, metabolically unhealthy status in midlife increased mortality risks by 31% hazard ratio 1.31; 95% confidence interval 1.12-1.53 and in late-life, by 18% (1.18;1.10-1.26) relative to metabolically healthy individuals. Midlife obesity increased the mortality risks by 30% (1.30;1.06-1.60) and late-life obesity by 15% (1.15; 1.04-1.27) relative to normal weight. In joint models, the BMI estimates were attenuated while those of MHS were less affected. Models including BMI-MHS categories revealed that, compared to metabolically healthy normal weight, the metabolically unhealthy obesity group had increased mortality risks by 53% (1.53;1.19-1.96) in midlife, and across all BMI categories in late-life (normal weight 1.12; 1.01-1.25, overweight 1.10;1.01-1.21, obesity 1.31;1.15-1.49). Mortality risk was decreased by 9% (0.91; 0.83-0.99) among those with metabolically healthy overweight in late-life.
MHS strongly influenced the BMI-mortality association, such that individuals who were metabolically healthy with overweight or obesity in mid- or late-life did not carry excess risks of mortality. Being metabolically unhealthy had a higher risk of mortality independent of their BMI.
This study aimed to investigate the association between shift work and incident dementia in two population-based cohorts from the Swedish Twin Registry (STR). The STR-1973 sample included 13,283 ...participants born 1926-1943 who received a mailed questionnaire in 1973 that asked about status (ever/never) and duration (years) of shift work employment. The Screening Across the Lifespan Twin (SALT) sample included 41,199 participants born 1900-1958 who participated in a telephone interview in 1998-2002 that asked about night work status and duration. Dementia diagnoses came from Swedish patient registers. Cox proportional-hazards regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI). Potential confounders such as age, sex, education, diabetes, cardiovascular disease and stroke were included in adjusted models. In genotyped subsamples (n = 2977 in STR-1973; n = 10,366 in SALT), APOE e4 status was considered in models. A total of 983 (7.4%) and 1979 (4.8%) dementia cases were identified after a median of 41.2 and 14.1 years follow-up in the STR-1973 and SALT sample, respectively. Ever shift work (HR 1.36, 95% CI 1.15-1.60) and night work (HR 1.12, 95% CI 1.01-1.23) were associated with higher dementia incidence. Modest dose-response associations were observed, where longer duration shift work and night work predicted increased dementia risk. Among APOE ɛ4 carriers, individuals exposed to ≥ 20 years of shift work and night work had increased dementia risk compared to day workers. Findings indicate that shift work, including night shift work, compared to non-shift jobs is associated with increased dementia incidence. Confirmation of findings is needed.
Age is a dominant risk factor for some of the most common neurological diseases. Biological ageing encompasses interindividual variation in the rate of ageing and can be calculated from clinical ...biomarkers or DNA methylation data amongst other approaches. Here, we tested the hypothesis that a biological age greater than one's chronological age affects the risk of future neurological diagnosis and the development of abnormal signs on clinical examination. We analysed data from the Swedish Adoption/Twin Study of Aging (SATSA): a cohort with 3175 assessments of 802 individuals followed-up over several decades. Six measures of biological ageing were generated: two physiological ages (created from bedside clinical measurements and standard blood tests) and four blood methylation age measures. Their effects on future stroke, dementia or Parkinson's disease diagnosis, or development of abnormal clinical signs, were determined using survival analysis, with and without stratification by twin pairs. Older physiological ages were associated with ischaemic stroke risk; for example one standard deviation advancement in baseline PhenoAgePhys or KDMAgePhys residual increased future ischaemic stroke risk by 29.2% hazard ratio (HR): 1.29, 95% confidence interval (CI) 1.06-1.58, P = 0.012 and 42.9% (HR 1.43, CI 1.18-1.73, P = 3.1 × 10-4), respectively. In contrast, older methylation ages were more predictive of future dementia risk, which was increased by 29.7% (HR 1.30, CI 1.07-1.57, P = 0.007) per standard deviation advancement in HorvathAgeMeth. Older physiological ages were also positively associated with future development of abnormal patellar or pupillary reflexes, and the loss of normal gait. Measures of biological ageing can predict clinically relevant pathology of the nervous system independent of chronological age. This may help to explain variability in disease risk between individuals of the same age and strengthens the case for trials of geroprotective interventions for people with neurological disorders.
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