Recontacting biobank participants to collect lifestyle, behavioural and cognitive information via online questionnaires: lessons from a pilot study within FinnGen

Objectives To recontact biobank participants and collect cognitive, behavioural and lifestyle information via a secure online platform. Design Biobank-based recontacting pilot study. Setting Three Finnish biobanks (Helsinki, Auria, Tampere) recruiting participants from February 2021 to July 2021. Participants All eligible invitees were enrolled in FinnGen by their biobanks (Helsinki, Auria, Tampere), had available genetic data and were >18 years old. Individuals with severe neuropsychiatric disease or cognitive or physical disabilities were excluded. Lastly, 5995 participants were selected based on their polygenic score for cognitive abilities and invited to the study. Among invitees, 1115 had successfully participated and completed the study questionnaire(s). Outcome measures The primary outcome was the participation rate among study invitees. Secondary outcomes included questionnaire completion rate, quality of data collected and comparison of participation rate boosting strategies. Results The overall participation rate was 18.6% among all invitees and 23.1% among individuals aged 18-69. A second reminder letter yielded an additional 9.7% participation rate in those who did not respond to the first invitation. Recontacting participants via an online healthcare portal yielded lower participation than recontacting via physical letter. The completion rate of the questionnaire and cognitive tests was high (92% and 85%, respectively), and measurements were overall reliable among participants. For example, the correlation (r) between self-reported body mass index and that collected by the biobanks was 0.92. Conclusion In summary, this pilot suggests that recontacting FinnGen participants with the goal to collect a wide range of cognitive, behavioural and lifestyle information without additional engagement results in a low participation rate, but with reliable data. We suggest that such information be collected at enrolment, if possible, rather than via post hoc recontacting.</p

Maternal blood cadmium, lead and arsenic levels, nutrient combinations, and offspring birthweight

Abstract Background Cadmium (Cd), lead (Pb) and arsenic (As) are common environmental contaminants that have been associated with lower birthweight. Although some essential metals may mitigate exposure, data are inconsistent. This study sought to evaluate the relationship between toxic metals, nutrient combinations and birthweight among 275 mother-child pairs. Methods Non-essential metals, Cd, Pb, As, and essential metals, iron (Fe), zinc (Zn), selenium (Se), copper (Cu), calcium (Ca), magnesium (Mg), and manganese (Mn) were measured in maternal whole blood obtained during the first trimester using inductively coupled plasma mass spectrometry. Folate concentrations were measured by microbial assay. Birthweight was obtained from medical records. We used quantile regression to evaluate the association between toxic metals and nutrients due to their underlying wedge-shaped relationship. Ordinary linear regression was used to evaluate associations between birth weight and toxic metals. Results After multivariate adjustment, the negative association between Pb or Cd and a combination of Fe, Se, Ca and folate was robust, persistent and dose-dependent (p < 0.05). However, a combination of Zn, Cu, Mn and Mg was positively associated with Pb and Cd levels. While prenatal blood Cd and Pb were also associated with lower birthweight. Fe, Se, Ca and folate did not modify these associations. Conclusion Small sample size and cross-sectional design notwithstanding, the robust and persistent negative associations between some, but not all, nutrient combinations with these ubiquitous environmental contaminants suggest that only some recommended nutrient combinations may mitigate toxic metal exposure in chronically exposed populations. Larger longitudinal studies are required to confirm these findings

Deep learning-based prediction of one-year mortality in the entire Finnish population is an accurate but unfair digital marker of aging

Abstract Background Accurately predicting short-term mortality is important for optimizing healthcare resource allocation, developing risk-reducing interventions, and improving end-of-life care. Moreover, short-term mortality risk reflects individual frailty and can serve as digital aging marker. Previous studies have focused on specific, high-risk populations. Predicting all-cause mortality in an unselected population incorporating both health and socioeconomic factors has direct public health relevance but requires careful fairness considerations. Methods We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population ( N = 5.4 million), including >8,000 features and spanning back up to 50 years. We used the area under the receiver operating characteristic curve (AUC) as a primary metric to assess model performance and fairness. Findings The model achieved an AUC of 0.944 with strong calibration, outperforming a baseline model that only included age and sex (AUC = 0.897). The model generalized well to different causes of death (AUC > 0.800 for 45 out of 50 causes), including COVID-19 which was not present in the training data. The model performed best among young females and worst in older males (AUC = 0.910 vs. AUC = 0.718). Extensive fairness analyses revealed that individuals belonging to multiple disadvantaged groups had the worst model performance, not explained by age and sex differences, reduced healthcare contact, or smaller training set sizes within these groups. Conclusion A deep learning model based on nationwide longitudinal multi-modal data accurately identified short-term mortality risk holding the potential for developing a population-wide in-silico aging marker. Unfairness in model predictions represents a major challenge to the equitable integration of these approaches in public health interventions