C16ORF70/Mytho promotes healthy ageing in C. elegans and prevents cellular senescence in mammals.

The identification of genes that confer either extension of lifespan or accelerate age-related decline was a step forward in understanding the mechanisms of ageing and revealed that it is partially controlled by genetics and transcriptional programs. Here we discovered that the human DNA sequence C16ORF70 encoded for a protein, named MYTHO (Macroautophagy and YouTH Optimizer), which controls life- and health-span. MYTHO protein is conserved from C. elegans to humans and its mRNA was upregulated in aged mice and elderly people. Deletion of the ortholog myt-1 gene in C. elegans dramatically shortened lifespan and decreased animal survival upon exposure to oxidative stress. Mechanistically, MYTHO is required for autophagy likely because it acts as a scaffold that binds WIPI2 and BCAS3 to recruit and assemble the conjugation system at the phagophore, the nascent autophagosome. We conclude that MYTHO is a transcriptionally regulated initiator of autophagy that is central in promoting stress resistance and healthy ageing.

Multilevel interactions between family and neighbourhood socioeconomic indices in childhood and later risks of self-harm and violent criminality in Denmark: a national cohort study.

BACKGROUND: A socioeconomically disadvantaged childhood has been associated with elevated self-harm and violent criminality risks during adolescence and young adulthood. However, whether these risks are modified by a neighbourhood's socioeconomic profile is unclear. The aim of our study was to compare risks among disadvantaged young people residing in deprived areas versus risks among similarly disadvantaged individuals residing in affluent areas. METHODS: We did a national cohort study, using Danish interlinked national registers, from which we delineated a longitudinal cohort of people born in Denmark between Jan 1, 1981, and Dec 31, 2001, with two Danish-born parents, who were alive and residing in the country when they were aged 15 years, who were followed up for a hospital-treated self-harm episode or violent crime conviction. A neighbourhood affluence indicator was derived based on nationwide income quartiles, with parental income and educational attainment indicating the socioeconomic position of each cohort member's family. Bayesian multilevel survival analyses were done to examine the moderating influences of neighbourhood affluence on associations between family socioeconomic position and sex-specific risks for the two adverse outcomes. FINDINGS: 1 084 047 cohort members were followed up for 12·8 million person-years in aggregate. Individuals of a low socioeconomic position residing in deprived neighbourhoods had a higher incidence of both self-harm and violent criminality compared with equivalently disadvantaged peers residing in affluent areas. Women from a low-income background residing in affluent areas had, on average, 95 (highest density interval 76-118) fewer self-harm episodes and 25 (15-41) fewer violent crime convictions per 10 000 person-years compared with women of an equally low income residing in deprived areas, whereas men of a low income residing in affluent areas had 61 (39-81) fewer self-harm episodes and 88 (56-191) fewer violent crime convictions per 10 000 person-years than men of a low income residing in deprived areas. INTERPRETATION: Even in a high-income European country with comprehensive social welfare and low levels of poverty and inequality, individuals residing in affluent neighbourhoods have lower risks of self-harm and violent criminality compared with individuals residing in deprived neighbourhoods. More research is needed to explore the potential of neighbourhood policies and interventions to reduce the harmful effects of growing up in socioeconomically deprived circumstances on later risk of self-harm and violent crime convictions. FUNDING: European Research Council, Lundbeck Foundation Initiative for Integrative Psychiatric Research, and BERTHA, the Danish Big Data Centre for Environment and Health funded by the Novo Nordisk Foundation Challenge Programme.

A multilevel Bayesian framework for predicting municipal waste generation rates.

Prediction of waste production is an essential part of the design and planning of waste management systems. The quality and applicability of such predictions depend heavily on model assumptions and the structure of the collected data. Ordinarily, municipal waste generation data are organized in hierarchical structures with municipal or county levels, and multilevel models can be used to generalize linear regression by directly incorporating the structure into the model. However, small amounts of data can limit the applicability of multilevel models and provide biased estimates. To cope with this problem, Bayesian estimation is often recommended as an alternative to frequentist estimation, such as least squares or maximum likelihood estimation. This paper proposes a multilevel framework under a Bayesian approach to model municipal waste generation with hierarchical data structures. Using a real-world dataset of municipal waste generation in Denmark, the predictive accuracy of multilevel models is compared to aggregated and disaggregated Bayesian models using socio-economic external variables. Results show that Bayesian multilevel models outperform the other models in prediction accuracy, based on the leave-one-out information criterion. A comparison of the Bayesian approach with its frequentist alternative shows that the Bayesian model is more conservative in coefficient estimation, with estimates shrinking to the grand mean and broader credible intervals, in contrast with narrower confidence intervals produced by the frequentist models.

Prediction of Autism Risk From Family Medical History Data Using Machine Learning: A National Cohort Study From Denmark.

Background: A family history of specific disorders (e.g., autism, depression, epilepsy) has been linked to risk for autism spectrum disorder (ASD). This study examines whether family history data could be used for ASD risk prediction. Methods: We followed all Danish live births, from 1980 to 2012, of Denmark-born parents for an ASD diagnosis through April 10, 2017 (N = 1,697,231 births; 26,840 ASD cases). Linking each birth to three-generation family members, we identified 438 morbidity indicators, comprising 73 disorders reported prospectively for each family member. We tested various models using a machine learning approach. From the best-performing model, we calculated a family history risk score and estimated odds ratios and 95% confidence intervals for the risk of ASD. Results: The best-performing model comprised 41 indicators: eight mental conditions (e.g., ASD, attention-deficit/hyperactivity disorder, neurotic/stress disorders) and nine nonmental conditions (e.g., obesity, hypertension, asthma) across six family member types; model performance was similar in training and test subsamples. The highest risk score group had 17.0% ASD prevalence and a 15.3-fold (95% confidence interval, 14.0-17.1) increased ASD risk compared with the lowest score group, which had 0.6% ASD prevalence. In contrast, individuals with a full sibling with ASD had 9.5% ASD prevalence and a 6.1-fold (95% confidence interval, 5.9-6.4) higher risk than individuals without an affected sibling. Conclusions: Family history of multiple mental and nonmental conditions can identify more individuals at highest risk for ASD than only considering the immediate family history of ASD. A comprehensive family history may be critical for a clinically relevant ASD risk prediction framework in the future.

The effect of early-life and adult socioeconomic position on development of lifestyle-related diseases.

BACKGROUND: Early-life socioeconomic position (SEP) is associated with lifestyle-related diseases in adulthood. However, evidence is lacking on the extent to which adult SEP mediates this association. METHODS: Time to either chronic obstructive pulmonary disease (COPD), cardiovascular disease or diabetes were assessed in the Danish population born between 1961 and 1971 (n = 793 674) from age 30 until 2015. Early-life position was assessed in 1981 (by parental) and again at age 30 (own) by four markers; income, occupation, education-divided into high, middle, low-and a combined score for all markers. Using a counterfactual approach, we estimated the total effect of early-life position on disease onset and the degree to which adult position mediated this effect. RESULTS: Results of the time-to-event analysis showed a gradient of all early-life markers on the risk of developing all lifestyle-related diseases. Notably, comparing those in the lowest to the highest educational position, the hazard of COPD was 130% higher for women [hazard ratio = 2.30(95% confidence interval = 2.20-2.41)] and 114% higher for men [2.14 (2.05-2.25)]. About 67%(63-70%) of the effect of educational position was mediated through adult position for COPD, 55% for cardiovascular disease and 50% for diabetes. For the combined score 44, 29 and 33%, respectively, was mediated. CONCLUSION: About one-tenth to two-thirds of the effect of early-life position is mediated by the position attained in adulthood. The degree mediated depend on the outcome investigated, gender and the social position marker used indicating that alternative pathways may play a key role in developing effective policies targeting early-life behaviours.

Assessing the relative importance of correlates of loneliness in later life. Gaining insight using recursive partitioning.

OBJECTIVES: Improving the design and targeting of interventions is important for alleviating loneliness among older adults. This requires identifying which correlates are the most important predictors of loneliness. This study demonstrates the use of recursive partitioning in exploring the characteristics and assessing the relative importance of correlates of loneliness in older adults. METHOD: Using exploratory regression trees and random forests, we examined combinations and the relative importance of 42 correlates in relation to loneliness at age 68 among 2453 participants from the birth cohort study the MRC National Survey of Health and Development. RESULTS: Positive mental well-being, personal mastery, identifying the spouse as the closest confidant, being extrovert and informal social contact were the most important correlates of lower loneliness levels. Participation in organised groups and demographic correlates were poor identifiers of loneliness. The regression tree suggested that loneliness was not raised among those with poor mental wellbeing if they identified their partner as closest confidante and had frequent social contact. CONCLUSION: Recursive partitioning can identify which combinations of experiences and circumstances characterise high-risk groups. Poor mental wellbeing and sparse social contact emerged as especially important and classical demographic factors as insufficient in identifying high loneliness levels among older adults.