Carsten Bøcker Pedersen

Background: Frontotemporal dementia (FTD) is a common form of early-onset dementia, and little is known about potential modifiable FTD risk factors. We sought to determine if exposure to ambient particulate matter with a diameter less than 2·5 μm (PM2·5) and exposure to nitrogen dioxide (NO2) was associated with risk of FTD diagnosis. Methods: We conducted a population-based, nested case-control study within a source population of all individuals aged 35-95 years residing in Denmark between January 1, 2001, and December 31, 2021. Cases were identified from hospital diagnoses with FTD and matched on sex and birth date with 10 controls randomly selected using risk-set sampling. We used conditional logistic regression to estimate incidence rate ratios (IRRs) for the associations of exposure to PM2·5 or NO2. Each matched case-control set formed a separate stratum. Models were adjusted for confounding factors and stratified by age and sex. Findings: There were 3,572 incident cases of FTD identified with 35,720 matched controls (median, interquartile range [IQR] age at FTD diagnosis: 74 years [67-80];50·4% females). In adjusted models, each 5 µg/m³ increase in PM2·5 was associated with a greater rate of FTD (IRR=1·80; 95%CI:1·42–2·27). The association was stronger in females (IRR=2·24; 95%CI:1·63-3·07) compared to males (IRR=1·42; 95%CI:1·03-1·96)(p=0·040). Each 10 µg/m³ increase in NO₂ was associated with 1·14-times greater rate of FTD (95%CI:1·02–1·28) with no significant difference between females (IRR=1·22; 95%-CI:1·05-1·41) and males (IRR=1·07;95%CI:0·92-1·24)(p=0·171). Interpretation: Exposure to PM2·5 and NO2 were each associated with a higher risk of FTD. These findings strengthen the evidence supporting an association of air pollution with neurodegeneration and specifically with FTD.

To examine the associations of neonatal 25-hydroxyvitamin D (25(OH)D) and vitamin D-binding protein (DBP), and their corresponding genetic predictors, with the risk of nine autoimmune disorders. We conducted a population-based cohort study of a random sample of individuals born in Denmark between 1981 and 2005, sourced from the iPSYCH2012 study. We measured 25(OH)D and DBP concentrations in neonatal dried blood spots. We identified individuals diagnosed with selected autoimmune disorders (multiple sclerosis, rheumatoid arthritis, psoriatic arthritis, type 1 diabetes mellitus, autoimmune thyroiditis, Graves’ disease, celiac disease, Crohn’s disease, and ulcerative colitis). Cox regression was employed to estimate hazard ratios (HRs) for any autoimmune disorder and for nine specific diagnoses, in relation to 25(OH)D, DBP, and their polygenic scores (PGSs). Among 20,404 eligible individuals, 757 (3.7%) developed an autoimmune disorder. Neither neonatal 25(OH)D nor DBP was associated with the risk of any autoimmune disorder, with HRs of 1.07 (95% CI, 0.99–1.15) and 0.99 (95% CI, 0.92–1.07) per standard deviation (SD) increase, respectively. Similarly, PGSs for 25(OH)D and DBP did not show an association with any autoimmune disorder, with HRs of 1.04 (95% CI, 0.97–1.12) and 0.98 (95% CI, 0.91–1.05) per SD increase, respectively. Findings were similar when exposures were analyzed in tertiles and across nine individual autoimmune disorders. Neonatal vitamin D is not associated with the risk of autoimmune disorders. Similarly, genetic predictors of vitamin D status, as reflected in PGSs for 25(OH)D and DBP, are not associated with the risk of autoimmune disorders. Graphical abstract

Background: Sibling comparison designs are increasingly used to strengthen causal claims about social exposures and health outcomes, yet methodologic challenges in interpreting their results remain insufficiently addressed. This study develops empirical approaches to help assess whether sibling comparison estimates provide reliable evidence for causal relationships. Methods: We used childhood family income and severe mental disorders in a Danish nationwide cohort (n = 643,623; 403,963 siblings born 1986–1996) as an example. We applied three complementary approaches: negative control analyses using pseudo-siblings (unrelated individuals with similar income differences as real siblings) to isolate exposure variability effects from shared familial confounding effects; assessment of sibling age structure, exposure correlation, and variation patterns to establish whether meaningful contrasts exist between siblings; and critical period assumption evaluation through age-specific income measurement. Results: Family income at age 14 was associated with decreased mental disorder risk in the population-wide analysis [adjusted hazard ratio (aHR) = 0.78; 95% confidence interval (CI): 0.76, 0.81] but showed no association using a sibling comparison design (aHR = 1.02; 95% CI: 0.94, 1.11). The pseudo-sibling cohort matched on income also showed substantial attenuation (aHR = 0.93; 95% CI: 0.85, 1.01), while pseudo-siblings not matched on income showed no attenuation. Income associations were similar across childhood measurement ages 0–14 (aHR range = 0.67–0.82). Conclusions: In this example, estimates from the sibling comparison design may reflect limited exposure variability within families and unmet life course model assumptions, rather than or in addition to the removal of shared familial confounding. The empirical approaches we developed help researchers distinguish methodologic factors from genuine null findings, and are available with R code for implementation.

Aim: To describe headache phenotypes and treatment patterns in adults with moderate to severe post-traumatic headache and a high burden of other post-concussion symptoms two to four months after mild traumatic brain injury. Methods: A cross-sectional study was conducted using patient-reported questionnaire data from 218 individuals, aged 18–60 years, with moderate to severe post-traumatic headache, a headache duration between two and four months, and a high burden of other post-concussion symptoms. The study was based on data from a cohort study with a nested randomized controlled trial called Get Going After Concussion 2.0. Individuals were recruited from emergency departments and general practitioners in Central Denmark Region (1.4 million inhabitants). Phenotyping was performed using an algorithm linking the questionnaire data to diagnostic criteria of primary headache disorders. Results: Among the 218 individuals, 62% had acute- and 38% had persistent post-traumatic headache. A migraine-like or probable migraine-like phenotype was identified in 88% of the individuals with no significant differences in phenotype distribution between the acute- and persistent headache subgroups (p = 0.414) or between sexes (p = 0.994). Headache frequency of ≥15 days per month was reported by 78% and average headache intensity was moderate or severe in 94% of all individuals. A pressing and/or tightening headache quality was the most frequent (85%). No significant differences were found between the acute- and persistent headache subgroups regarding pharmacological treatment use or efficacy (p = 0.776). Non-opioid analgesics had been used by 88% of all individuals and 81% were still using them; 52% reported lack of efficacy. Triptans had been or were currently used by 4% of all individuals. Preventive medication, used by 7% of the study population, was reported to have lack of efficacy but all continued treatment. Probable medication overuse headache was found in 27% of the individuals. Overall, 33% had tried two or more non-pharmacological treatments, and 6% had tried between four and six. Conclusion: A migraine-like phenotype was the most common presentation in adults with moderate to severe post-traumatic headache and a high symptom burden of other post-concussion symptoms two to four months after trauma. Overall, limited effect of both pharmacological and non-pharmacological treatment was reported. Preventive medication and triptans were used by very few individuals, despite being recommended in current treatment guidelines. The study population exhibited a high health care-seeking behavior, and a large proportion had probable medication overuse headache. Consequently, further insights into disease-specific treatment approaches of post-traumatic headache is needed.

Background: Psychiatric disorders represent a significant global health burden with complex etiologies involving both genetic and environmental factors. However, while the main effects of genetic and environmental factors are frequently studied, their interplay in shaping psychiatric disorder risk remains poorly understood. This study investigates the interaction between polygenic scores (PGS) as proxies for genetic risk and environmental factors in the risk of psychiatric disorders. Method: This study utilized the iPSYCH case-cohort sample (n = 141,265). Environmental factors, including region, urbanicity, parental socioeconomic status, parental age, parental psychiatric history and early-life exposures: autoimmune disease, brain injury, and central nervous system (CNS) infections, were obtained from Danish nationwide registers. Logistic regression was used to examine the effects of targeted disorder-specific PGSs, environmental factors and their interactions on psychiatric disorders. Analyses were adjusted for age, sex and ancestral principal components to account for population stratification. Results: Both PGS and environmental factors were associated with psychiatric disorders. We found limited evidence of gene–environment interactions across the investigated psychiatric disorders. Most interaction terms were small and not statistically significant, but a few remained significant. These included a smaller PGS association with attention deficit hyperactivity disorder in Southern Denmark compared with the Capital Region, a reduced PGS association with bipolar disorder in the medium and lowest parental income groups compared with the highest income group, and a weaker PGS association with major depressive disorder in individuals with parental psychiatric history compared with those without such history. In contrast, a larger PGS association with schizophrenia was observed in the paternal age group 31–35 years compared with the 26–30 years reference group, and a stronger PGS association with anorexia nervosa in North Denmark compared with the Capital Region. Conclusion: Genetic liability for psychiatric disorders, captured through PGS, was associated with mental disorder risk across environmental contexts. Only a few gene–environment interactions were significant, and these were modest and mental disorder-specific. Overall, the findings highlight the difficulty of detecting robust gene–environment interactions and the need for studies of sufficient scale and statistical power to identify subtle gene–environment effects and improve understanding of psychiatric disorder etiology.

Background Psychiatric disorders represent a significant global health burden with complex etiologies involving both genetic and environmental factors. However, while the main effects of genetic and environmental factors are frequently studied, their interplay in shaping psychiatric disorder risk remains poorly understood. This study investigates the interaction between polygenic scores (PGS) as proxies for genetic risk and environmental factors in the risk of psychiatric disorders. Method This study utilized the iPSYCH case-cohort sample (n = 141,265). Environmental factors, including region, urbanicity, parental socioeconomic status, parental age, parental psychiatric history and early-life exposures: autoimmune disease, brain injury, and central nervous system (CNS) infections, were obtained from Danish nationwide registers. Logistic regression was used to examine the effects of targeted disorder-specific PGSs, environmental factors and their interactions on psychiatric disorders. Analyses were adjusted for age, sex and ancestral principal components to account for population stratification. Results Both PGS and environmental factors were associated with psychiatric disorders. We found limited evidence of gene–environment interactions across the investigated psychiatric disorders. Most interaction terms were small and not statistically significant, but a few remained significant. These included a smaller PGS association with attention deficit hyperactivity disorder in Southern Denmark compared with the Capital Region, a reduced PGS association with bipolar disorder in the medium and lowest parental income groups compared with the highest income group, and a weaker PGS association with major depressive disorder in individuals with parental psychiatric history compared with those without such history. In contrast, a larger PGS association with schizophrenia was observed in the paternal age group 31–35 years compared with the 26–30 years reference group, and a stronger PGS association with anorexia nervosa in North Denmark compared with the Capital Region. Conclusion Genetic liability for psychiatric disorders, captured through PGS, was associated with mental disorder risk across environmental contexts. Only a few gene–environment interactions were significant, and these were modest and mental disorder-specific. Overall, the findings highlight the difficulty of detecting robust gene–environment interactions and the need for studies of sufficient scale and statistical power to identify subtle gene–environment effects and improve understanding of psychiatric disorder etiology.

Summary: Research in the familial aggregation of diseases and traits utilise information on probands, and their relevant health information enriched with similar information for their family members of interest. The genealogy is typically generated from trio information in registers and biobanks. However it can be tedious and error prone to identify family members other than first-degree relatives. Here, we present a graph-based approach to effectively identify family members of arbitrary degree of relatedness, as well as the means to attach any desired information to each individual for downstream analysis and a function to efficiently calculate a kinship matrix for the identified family members and convert identified family members from a graph back into trio information. Availability and Implementation: The R package where these new functionalities are implemented is available on GitHub ( https://github.com/EmilMiP/LTFHPlus ) and on CRAN ( https://cran.r-project.org/web/packages/LTFHPlus/index.html ).

Background: The emerging use of biomarkers in research and tailored care introduces a need for information about the association between biomarkers and basic demographics and lifestyle factors revealing expectable concentrations in healthy individuals while considering general demographic differences. Methods: A selection of 47 biomarkers, including markers of inflammation and vascular stress, were measured in plasma samples from 9876 Danish Blood Donor Study participants. Using regression models, we examined the association between biomarkers and sex, age, Body Mass Index (BMI), and smoking. Results: Here we show that concentrations of inflammation and vascular stress biomarkers generally increase with higher age, BMI, and smoking. Sex-specific effects are observed for multiple biomarkers. Conclusion: This study provides comprehensive information on concentrations of 47 plasma biomarkers in healthy individuals. The study emphasizes that knowledge about biomarker concentrations in healthy individuals is critical for improved understanding of disease pathology and for tailored care and decision support tools.