Observational studies have found shorter leukocyte telomere length (TL) to be a risk factor for coronary heart disease (CHD), and recently the association was suggested to be causal. However, the ...relationship between TL and common metabolic risk factors for CHD is not well understood. Whether these risk factors could explain pathways from TL to CHD warrants further attention.
To examine whether metabolic risk factors for CHD mediate the causal pathway from short TL to increased risk of CHD using a network Mendelian randomization design.
Summary statistics from several genome-wide association studies were used in a 2-sample Mendelian randomization study design. Network Mendelian randomization analysis-an approach using genetic variants as the instrumental variables for both the exposure and mediator to infer causality-was performed to examine the causal association between telomeres and CHD and metabolic risk factors. Summary statistics from the ENGAGE Telomere Consortium were used (n=37 684) as a TL genetic instrument, CARDIoGRAMplusC4D Consortium data were used (case=22 233 and control=64 762) for CHD, and other consortia data were used for metabolic traits (fasting insulin, triglyceride, total cholesterol, low-density lipoprotein cholesterol, fasting glucose, diabetes mellitus, glycohemoglobin, body mass index, waist circumference, and waist:hip ratio). One-unit increase of genetically determined TL was associated with -0.07 (95% confidence interval, -0.01 to -0.12;
=0.01) lower log-transformed fasting insulin (pmol/L) and 21% lower odds (95% confidence interval, 3-35;
=0.02) of CHD. Higher genetically determined log-transformed fasting insulin level was associated with higher CHD risk (odds ratio, 1.86; 95% confidence interval, 1.01-3.41;
=0.04).
Overall, our findings support a role of insulin as a mediator on the causal pathway from shorter telomeres to CHD pathogenesis.
Frailty index (FI) is a well-established predictor of all-cause mortality, but less is known for cause-specific mortality and whether familial effects influence the associations. Middle-aged ...individuals are also understudied for the association between FI and mortality. Furthermore, the population mortality impact of frailty remains understudied.
We estimated the predictive value of FI for all-cause and cause-specific mortality, taking into account familial factors, and tested whether the associations are time-dependent. We also assessed the proportion of all-cause and cause-specific deaths that are attributable to increased levels of frailty. We analyzed 42,953 participants from the Screening Across the Lifespan Twin Study (aged 41-95 years at baseline) with up to 20 years' mortality follow-up. The FI was constructed using 44 health-related items. Deaths due to cardiovascular disease (CVD), respiratory-related causes, and cancer were considered in the cause-specific analysis. Generalized survival models were used in the analysis.
Increased FI was associated with higher risks of all-cause, CVD, and respiratory-related mortality, with the corresponding hazard ratios of 1.28 (1.24, 1.32), 1.31 (1.23, 1.40), and 1.23 (1.11, 1.38) associated with a 10% increase in FI in male single responders, and 1.21 (1.18, 1.25), 1.27 (1.15, 1.34), and 1.26 (1.15, 1.39) in female single responders. No significant associations were observed for cancer mortality. No attenuation of the mortality associations in unrelated individuals was observed when adjusting for familial effects in twin pairs. The associations were time-dependent with relatively greater effects observed in younger ages. Before the age of 80, the proportions of deaths attributable to FI levels > 0.21 were 18.4% of all-cause deaths, 25.4% of CVD deaths, and 20.4% of respiratory-related deaths in men and 19.2% of all-cause deaths, 27.8% of CVD deaths, and 28.5% of respiratory-related deaths in women. After the age of 80, the attributable proportions decreased, most notably for all-cause and CVD mortality.
Increased FI predicts higher risks of all-cause, CVD, and respiratory-related mortality independent of familial effects. Increased FI presents a relatively greater risk factor at midlife than in old age. Increased FI has a significant population mortality impact that is greatest through midlife until the age of 80.
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.