Ingvild Saltvedt

Alzheimer’s disease (AD) is the most common cause of dementia, with global case numbers projected to reach 153 million in 2050¹. AD is highly heritable, with twin-based heritability estimates of 60–80%². Although 1,200 causal loci are predicted to exist for AD³, approximately 80 loci have been associated with AD in two recent studies^4,5. Low single-nucleotide polymorphism (SNP)-based heritability estimates have prompted the suggestion that little further common-variant risk remains to be found⁶. Here we analysed data from 109,479 cases, 74,141 proxy cases, 2,131,799 controls and 499,708 proxy controls from diverse ancestries, identifying 118 loci in a multi-ancestry analysis and 9 additional loci in ancestry-specific analyses, 48 of which are new. We identified new AD risk genes, prioritized potential drug targets, and found that genes upregulated in microglia and downregulated in neurons in cases compared with controls were enriched for AD-associated genetic risk. Moreover, we improved polygenic prediction and estimated a SNP heritability of 16%, nearly threefold higher than previous estimates. Together, our findings show that the common-variant contribution to AD has been substantially underestimated, and reveal new molecular and cellular mechanisms as well as specific targets for future drug development research.

Previous research has been inconsistent regarding the longitudinal relationship between physical activity and cognition after a stroke. In this study, we hypothesized that (i) higher levels of objectively measured daily step counts would be associated with better cognitive function at a group level (between-person level), and (ii) at an individual level, changes from expected step counts would be bi-directionally associated with changes from expected cognitive performance (within-person level) at the same and subsequent time points. We used data from 468 participants of the Norwegian Cognitive Impairment after Stroke study (Nor-COAST), a large multicentre cohort study with follow-up at 3, 18 and 36 months post-stroke. At each follow-up, average step count over 7 days was recorded using accelerometers, and cognitive performance was assessed with the Montreal Cognitive Assessment (MoCA), the Trail Making Test A and B (TMT-A, TMT-B), and the Word List Memory and Recall Test (delayed recall). Adjusted random intercept cross-lagged panel models (RI-CLPMs) were implemented. One RI-CLPM was built for each cognitive outcome, separating between- from within-person associations. To balance baseline characteristics of included and excluded participants in Nor-COAST and to account for selection bias, inverse probability weighting was performed using age, sex, and baseline functional independence. Between-persons, we found a time-invariant association between step count and TMT-A (β = 0.24, 95% CI: 0.09 to 0.40) and TMT-B (β = 0.22, 95% CI: 0.09 to 0.34); after correcting for multiple comparison the association with MoCA was not significant (β = 0.14, 95% CI: 0.01 to 0.28), and there was no association with the Word List Memory and Recall Test (β = 0.09, 95% CI: −0.04 to 0.22). At the within-person level, we observed neither cross-sectional nor cross-lagged associations between step count and any of the cognitive tests. The RI-CLPM fitted the data appropriately and had robust testing models with and without inverse probability weighting. After a stroke, people who, on average, took more steps tended to also perform better on TMT-A and TMT-B scores. However, at an individual level, a person who walked more (or had better cognitive scores) than expected did not have a higher likelihood of also performing better on cognitive tests (or walking more) than their expected average, respectively. Therefore, the observed association between step count and cognitive function after stroke might be explained by consistent individual differences over time, rather than by temporary changes within an individual's typical level of physical activity or cognition.

BackgroundSystemic inflammation is a crucial component of the acute response to stroke and is associated with poor clinical outcomes. However, its connection to long-term structural brain changes as a pathophysiological explanation for poor outcomes remains incompletely understood.MethodsA total of 241 stroke patients (44.8% women) were included from the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study, with a mean age of 72.8 years and a mean NIHSS score of 3.1. Inflammatory biomarkers were collected from blood during the acute phase (median 4 days after stroke). MRI was performed during the acute stroke phase of the stroke and at 18- and 36-month follow-up, including measures of white matter hyperintensity (WMH) volume, global and hippocampal gray matter volume, and brain age gap (BAG). Relationships between inflammatory biomarkers and brain measures were assessed using linear mixed-effects models.ResultsHigher levels of IL-2, IL-6, MIP-1α, MIP-1β, and IP10 were associated with lower baseline global gray matter volume and higher baseline BAG. IL-1RA, IL-4, IL-5, IL17A, TCC, bFGF, and G-CSF were associated with WMH progression over 36 months. No inflammatory biomarker was associated with baseline hippocampal volume, whereas bFGF and G-CSF were associated with greater hippocampal volume decline over time.ConclusionAcute systemic inflammation after stroke was associated with signs of brain atrophy and white matter damage, characteristics of cerebral atherosclerotic and small vessel disease, which are risk factors for subsequent cerebrovascular events and vascular dementia. In contrast, evidence for an association with hippocampal volume, a hallmark of Alzheimer’s disease, was limited.

Background and Aims: The 2021 ESC guidelines on cardiovascular (CV) disease prevention recommend the SMART-REACH lifetime risk model to guide treatment decisions in patients with established atherosclerotic CV disease. The aim was to develop the SMART-REACH2 model for estimating lifetime risk of recurrent CV events and treatment benefits in patients with established atherosclerotic CV disease, with systematic recalibration to the four European and other global risk regions. Methods: SMART-REACH2 was derived in 8708 individuals aged 40–90 years with coronary, cerebrovascular, peripheral artery disease and/or abdominal aortic aneurysm from the UCC-SMART cohort. Sex-stratified, cause-specific Cox models for recurrent CV events and non-CV death were fitted using age as timescale and routinely available predictors. Recurrent CV events were defined as a composite of myocardial infarction, stroke, or CV death. Recalibration was based on representative cohorts per risk region. External validation was performed in 2 085 780 patients from 54 countries; model performance was assessed by calibration plots and Harrell's C-statistic. Results: In the derivation cohort, 2057 recurrent CV events occurred over a median follow-up of 8.5 years (25th–75th: 4.3–13.0). In external validation, 307 706 events occurred. The pooled C-statistic was 0.68 (95% confidence interval 0.66–0.69) and ranged from 0.66 (0.64–0.69) for European low-risk region up to 0.72 (0.66–0.78) for Latin America, with adequate calibration across risk regions. Performance was consistent across sexes and CV disease subtypes. Using SMART-REACH2, estimated potential gains in CV disease-free life expectancy for a 50-year-old example patient receiving intensified preventive treatment (15 mmHg systolic blood pressure and 1.0 mmol/L low-density lipoprotein cholesterol reduction) ranged from 2 years in the low-risk region to 4.4 years in the very-high-risk region. Conclusions: The updated SMART-REACH2 model accounts for geographical and sex-specific variations and allows estimation of short-term and lifetime risk of recurrent CV events and treatment benefits, facilitating shared decision-making as recommended by guidelines.

Aims: Guidelines recommend using the SMART2 model, estimating the risk of recurrent cardiovascular (CV) events, to support treatment decisions in patients with established atherosclerotic CV disease (ASCVD). They further outline that adding biomarkers, comorbidities, anthropometric, and social factors may improve these predictions. This study aims to investigate the added predictive value of guideline-outlined factors including biomarkers, comorbidities, anthropometric, and social factors on top of the SMART2 model using an approach enabling their use as add-on predictors. Methods and results: Patients aged 40–80 with ASCVD were included from 11 cohorts (n = 179 382 with 25 789 recurrent CV events). Additional factors included biomarkers [troponin I, N-terminal pro-B-type natriuretic peptide (NT-proBNP), albuminuria], comorbidities (heart failure, atrial fibrillation, coronary multivessel disease), anthropometric measurements [body mass index (BMI), waist and hip circumference], social (employment, education), and other factors (former smoking, parental CV history). Cross-cohort availability of these factors ranged from two cohorts for albuminuria to all cohorts for BMI. These factors were assessed as add-on predictors to the SMART2 model using Fine–Gray models with SMART2 coefficients as offset with recurrent CV events as the primary outcome (non-fatal myocardial infarction or stroke, or CV death). Added predictive value was assessed through cohort cross-validation by change (Δ) in C-statistic, calibration, and net benefit through decision curve analysis. Subdistribution hazard ratios for additional factors ranged from 0.77 (95% confidence interval 0.75–0.80) for employment status to 1.69 (1.63–1.76) for heart failure history. ΔC-statistic was largest for NT-proBNP [0.0127 (0.0060–0.0193)] and troponin I [0.0100 (0.0020–0.0181)], with statistically significant but smaller ΔC-statistics for employment, heart failure, and atrial fibrillation. Calibration was adequate before and after integration of additional factors. Decision curve analysis demonstrated added net benefit beyond SMART2 for NT-proBNP, heart failure history, atrial fibrillation, albuminuria, current employment, coronary multivessel disease, and education level across clinically relevant thresholds up to 40% predicted risk. Conclusion: The flexible add-on of guideline-outlined factors on top of SMART2 enables more personalized and improved estimation of recurrent CV event risk in patients with established ASCVD.

Purpose: To document use and impact of potentially inappropriate medications on two-year progression of dementia in individuals with cognitive declines. Methods: A retrospective study of 397 patients with Mild Cognitive Impairment (MCI) or dementia diagnosed and followed-up in outpatient memory clinics in Norway during 2009 − 18. Beers (2019)- and STOPP-2 criteria were used to identify Potentially Inappropriate Medications (PIMcogs) in individuals with cognitive impairments at baseline and two-year-follow-up. PIMcog use in terms of dementia severity, cognitive function, and neuropsychiatric and depressive symptoms were analyzed in regression models. Results: The prevalence of PIMcogs increased from 16% at baseline to 23% at follow-up. PIMcog users were more likely to be women (63.5%), and they used more drugs, with a median of 5 drugs at baseline and 4 drugs at follow-up, compared to non-users who had a median of 3 used drugs at both time points. PIMcog users had higher median Neuropsychiatric Inventory severity sum scores (6 [3.0–11.0] versus 4.0 [2.0–7.0]) and median Cornell Scale for Depression in Dementia scores (6.5 [3.0–11.0] versus 4.0 [1.0–7.0]) compared to non-users at follow-up ( p ≤ 0.002). PIMcog users exhibited more severe dementia, with a Clinical Dementia Rate-Sum of Boxes (CDR-SB) score of 7.0 (4.0–13.0) compared to 6.0 (3.5–10.0) in non-users. The median annual increase in CDR-SB was one unit, and PIMcog use at follow-up was significantly associated with more rapid progression of dementia severity. Conclusion: Faster dementia progression was documented among PIMcog users although, the prevalence of PIMcogs was generally low in Norwegian memory clinic patients with cognitive impairments.

Background: Post-stroke physical activity (PA) behavior may partly explain inter-individual differences in cortical and sub-cortical brain volumes and brain age estimates. Objective: To investigate longitudinal associations of post-stroke PA behavior with structural brain MRI features. Methods: Data were from a multicenter longitudinal cohort study. PA estimates were based on accelerometer measurements. Separate linear mixed models assessed average daily step count at 18 and 36 months, and longitudinal PA trajectory groups as measured at 3, 18, and 36 months after stroke, as primary and secondary exposures. Dependent variables included brain age gap (BAG), representing the discrepancy between brain MRI predicted age and chronological age, and MRI-based cortical, hippocampal, and thalamic volumes at 18- and 36 months post-stroke. Models accounted for age, sex, education, stroke severity, intracranial volume, and MRI scanner. Results: We included 146 participants (age, mean [SD]: 70.3 [11.1]; 45.7% female) with predominantly mild strokes. Every +1000 steps/day were associated with −1.15 (95% CI: −1.76 to −0.53) lower BAG, 2.63 mL (95% CI: 0.31-5.00) larger cortical volume, and 0.07 mL (95% CI: 0.03-0.11) larger hippocampal volume. The association between step/day and thalamic volume was curvilinear, with the largest volumes observed at 4700 steps/day. Out of 4 PA trajectory groups, participants in the most active group had −7.44 years (95% CI: −2.86 to −12.01) lower BAG and 0.90 mL (95% CI: 1.48-0.33) larger thalamic volumes than the least active group. Conclusions: Higher PA levels post-stroke were associated with larger brain volumes and younger-appearing brains.

Aims: Guidelines recommend using cardiovascular (CV) risk prediction models to support treatment decisions. Psychological factors such as depression, anxiety, and insomnia may affect CV risk. The aim is to evaluate the added value of these psychological factors on top of the SMART2 model for estimating 10-year risk of recurrent CV events in patients with atherosclerotic CV disease. Methods and results: Patients aged 40–80 with atherosclerotic CV disease were included from the UCC-SMART, HUNT3, HUNT4, NORCOR, and Nor-COAST cohorts. Potential added value of (self-reported) symptoms, diagnoses, and treatment for depression, anxiety, and insomnia on top of the SMART2 was investigated using Fine and Gray models, with SMART2 coefficients as an offset. Added value was assessed by change (Δ) in C-statistic and calibration. A total of 20 050 patients were eligible, with 2987 experiencing recurrent CV events. Psychological factors were present in 3–9%, 3–45%, and 10–14% of patients for diagnosis, self-reported symptoms, and prescribed treatment, respectively. No psychological factors were significantly associated with recurrent CV events on top of SMART2 and performance in patients with psychological factors was adequate (C-statistics 0.61–0.70). Added value of psychological factors was minimal, with the ΔC-statistic ranging from −0.0003 [95% confidence interval: −0.0005–0.0001] for symptoms of depression, to 0.0011 [0.0011–0.0011] for prescribed treatment with anxiolytics. There was no relevant difference in calibration. Conclusion: SMART2 model reliably estimates recurrent CV event risk in patients with psychological factors. Psychological factors have no added value to CV risk prediction when integrated on top of the SMART2 risk score.

Background/Objectives: The burden of disease caused by fragility fractures, and hip fractures in particular, is significant both from a personal and a societal perspective. Digital technologies such as wearable devices now allow a real-world assessment of walking and physical mobility, describing temporal and spatial measures. Built on a transparent technical validation, the Mobilise-D consortium validated a set of digital mobility outcomes (DMOs) alongside a longitudinal Clinical Validation Study (CVS). This article provides a description of DMOs along with supervised mobility assessment and patient-reported outcomes collected at entry of the CVS study in a sample of home-dwelling participants with a proximal femoral fracture (PFF) recruited within one year from surgery. Methods: Participants who underwent surgical treatment for a low-energy PFF were recruited from five sites in three high-income European countries. The baseline visit encompassed a clinical assessment (~3 hours) and subsequent 7-day measurement of real-world mobility with a single wearable device. We categorized participants into four recovery phases according to the assessment time after surgery: acute phase (≤14 days after surgery, hospital stay), post-acute phase (15-42 days after surgery, mostly rehabilitation), extended recovery phase (43 – 182 days post-surgery, typically post-discharge at home) and long-term recovery phase (183 – 365 days post-surgery). All variables were reported descriptively for each phase using mean and standard deviation or median and 25th-75th percentile depending on their distribution. Results: We enrolled 564 PFF participants (66% women). The mean age was 77.5 (SD 9.6) years. Almost 90% of all participants (n=505) had a valid digital mobility assessment (wear time of >12 hours per day and at least 3 valid days). DMO domains such as amount (e.g. number of steps), pace (e.g. walking speed) and pattern (bout distribution) demonstrated large differences across phases. Conclusions: The observed variation in walking amount, pace and pattern across recovery phases indicate that DMOs provide a granular analysis of real-world mobility of hip fracture survivors. When confirmed by longitudinal analyses, including results on minimal important differences, the use of selected DMOs will provide a novel approach for monitoring, predictive modeling, prognosis, stratification and evaluation of clinical trials and hip fracture services.