Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as ...this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.
We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one.
In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.
ClinicalTrials.gov NCT03814915.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Assessing the causal effects of individual dietary macronutrients and cardiometabolic disease is challenging because distinguish direct effects from those mediated or confounded by other factors is ...difficult. To estimate these effects, intake of protein, carbohydrate, sugar, fat, and its subtypes were obtained using food frequency data derived from a Swedish population-based cohort (n~60,000). Data on clinical outcomes (i.e., type 2 diabetes (T2D) and cardiovascular disease (CVD) incidence) were obtained by linking health registry data. We assessed the magnitude of direct and mediated effects of diet, adiposity and physical activity on T2D and CVD using structural equation modelling (SEM). To strengthen causal inference, we used Mendelian randomization (MR) to model macronutrient intake exposures against clinical outcomes. We identified likely causal effects of genetically predicted carbohydrate intake (including sugar intake) and T2D, independent of adiposity and physical activity. Pairwise, serial- and parallel-mediational configurations yielded similar results. In the integrative genomic analyses, the candidate causal variant localized to the established T2D gene
. These findings may be informative when considering which dietary modifications included in nutritional guidelines are most likely to elicit health-promoting effects.
People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining a priori who is most sensitive may help optimize the timing, design, and delivery of ...preventative interventions. We aimed to ascertain a person’s degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual’s cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as ‘sensitive’ to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.6 (95% CI: 1.3, 1.91)), compared with the general population. No differences were observed for type 2 diabetes (T2D) risk. Here, we report a novel approach to identify individuals who are especially sensitive to adverse lifestyle exposures and who are at higher risk of subsequent cardiovascular events. Early preventive interventions may be needed in this subgroup.
Aims
Exposure to extraordinary traumatic experience is one acknowledged risk factor for drug use. We aim to analyse the influence of potentially life‐changing childhood stressors, experienced ...second‐hand, on later drug use disorder in a national population of Swedish adolescent and young adults (aged 15–26 years).
Design
We performed Cox proportional hazard regression analyses, complemented with co‐relative pair comparisons.
Setting
Sweden.
Participants
All individuals in the Swedish population born 1984–95, who were registered in Sweden at the end of the calendar year that they turned 14 years of age. Our follow‐up time (mean 6.2 years; range 11 years) started at the year they turned 15 and continued to December 2011 (n = 1 409 218).
Measurements
Our outcome variable was drug use disorder, identified from medical, legal and pharmacy registry records. Childhood stressors, as per DSM‐IV stressor criteria, include death of an immediate family member and second‐hand experience of diagnoses of malignant cancer, serious accidental injury and victim of assault. Other covariates include parental divorce, familial psychological wellbeing and familial drug and alcohol use disorders.
Findings
After adjustment for all considered confounders, individuals exposed to childhood stressors ‘parental death’ or ‘parental assault’ had more than twice the risk of drug use disorder than those who were not hazard ratio (HR) = 2.63 (2.23–3.09) and 2.39 (2.06–2.79), respectively.
Conclusions
Children aged under 15 years who experience second‐hand an extraordinary traumatic event (such as a parent or sibling being assaulted, diagnosed with cancer or dying) appear to have approximately twice the risk of developing a drug use disorder than those who do not.
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BFBNIB, DOBA, FSPLJ, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Smoking prevalence across high-income countries such as the United Kingdom has significantly decreased over the past few decades; this decrease, however, has not occurred uniformly across social ...strata. The highest concentrations of smokers are currently found in lower-income groups. Lack of access to material resources and differing social norms have been cited as possible causes of this imbalance in smoking behaviour. Social capital, measured by trust and levels of community participation, has also been postulated to influence health behaviour.
Data from the British Household Panel Survey were used to identify smoking and non-smoking cohorts at baseline (N = 10,512); from these, individuals whose smoking behaviour had changed (the dependent variable) were identified. Measures of social capital, income, employment and marital status, and considered confounders were tested for associations with changes in smoking behaviour over a 2-year period. Both bivariate and multivariate models were utilized to elicit associations.
Only marital and employment status, along with social capital measures, remained significantly associated with changes in smoking behaviour. Individual/household income, baseline social class and general/psychological health failed to demonstrate any significant association with changes in smoking status.
Support mechanisms (via marriage and employment) and elements social capital (measured by 'trust' and 'social participation') are independently and positively associated with smoking cessation; continual lack of active social participation and remaining single are associated with smoking initiation. Smoking interventions should consider increased participation as an intrinsic part of their design.
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NUK, OILJ, UL, UM, UPUK, VSZLJ
Aims/hypothesis
Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and ...cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic.
Methods
In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA
1c
, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres. Finally, we compared the time to insulin requirement for each cluster.
Results
Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6–90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol MDH cluster), and the other not having any extreme characteristic (mild diabetes MD). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression.
Conclusions/interpretation
Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA
1c
, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.
Graphical abstract
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five ...diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (
= 12,828), metabolomic (
= 2,945), lipidomic (
= 2,593), and proteomic (
= 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.
The London public transport suicide bombings, which occurred on 7th July 2005, were described as the worst single terrorist atrocity on British soil to date. Past acts of terrorism have been ...associated with deterioration in population mental health. They may also negatively impact levels of social capital, which is considered a buffer against poor mental health outcomes. By employing panel data from the British Household Panel Survey and following the
individuals (N
=9287) three times over a five-year period (2003, 2005 and 2007), the aim of this longitudinal multilevel study was to investigate: (i) the impact of terrorism on individual-level social capital (generalised trust and social participation) across the UK; and (ii) the buffering effects of social capital on psychological wellbeing (GHQ-12). By comparing 2005 and 2007 covariate values (including the two social capital proxies) against their pre-terror baseline (2003) measurements in two separate multilevel logistic regression models, we examined the immediate and longer-term effects of the 2005 attacks on our GHQ-12 outcome. Compared to baseline, generalised trust dropped from 44% to 36% immediately post-terror attacks in 2005, while local participation increased from 45.8% to 47.5%. Social capital levels started to return to baseline levels by 2007, yet both proxies maintained independent buffering effects against poor GHQ-12 scores in years 2005 and 2007. From this empirical evidence, it seems that though generalised trust levels are negatively affected by acts of terrorism, the accompanying increase in local active participation may aid in the re-establishment of societal norms and beliefs in later years. Decision makers should be aware that such atrocities may negatively impact on populations' generalised trust in the shorter-term. To safeguard against losing this buffer against poor mental health outcomes, local active participation should be encouraged.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract Background The societal consequences of drug abuse (DA) are severe and well documented, the World Health Organization recommending tracking of population trends for effective policy ...responses in treatment of DA and delivery of health care services. However, to correctly identify possible sources of DA change, one must first disentangle three different time-related influences on the need for treatment due to DA: age effects, period effects and cohort effects. Methods We constructed our main Swedish national DA database (spanning four decades) by linking healthcare data from the Swedish Hospital Discharge Register to individuals, which included hospitalisations in Sweden for 1975–2010. All hospitalized DA cases were identified by ICD codes. Our Swedish national sample consisted of 3078,129 men and 2921,816 women. We employed a cross-classified multilevel logistic regression model to disentangle any net age, period and cohort effects on DA hospitalization rates. Results We found distinct net age, period and cohort effects, each influencing the predicted probability of hospitalisation for DA in men and women. Peak age for DA in both sexes was 33–35 years; net period effects showed an increase in hospitalisation for DA from 1996 to 2001; and in birth cohorts 1968–1974, we saw a considerable reduction (around 75%) in predicted probability of hospitalisation for DA. Conclusions The use of hospital admissions could be regarded as a proxy of the population's health service use for DA. Our results may thus constitute a basis for effective prevention planning, treatment and other appropriate policy responses.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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