Search

Context The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. Objective We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. Methods We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. Results Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. Conclusion These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D. [ABSTRACT FROM AUTHOR]

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence. [ABSTRACT FROM AUTHOR]

Context: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity.Objective: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies.Design: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models.Results: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10-9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10-9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity.Conclusion: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity. [ABSTRACT FROM AUTHOR]

In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale. The main novelties of the technique presented are that (i) the small-scale mapping produced is perfectly consistent with the aggregates -regional or national- observed in the sample, and (ii) it does not require imposing strong distributional assumptions. The methodology suggested here follows the basics presented on Golan (2018) by adapting a cross-moment constrained Generalized Maximum Entropy (GME) estimator to the spatial disaggregation problem. This procedure is compared with the equivalent methodology of Tarozzi and Deaton (2009) by means of numerical experiments, providing a comparatively better performance. Additionally, the practical implementation of the methodology proposed is illustrated by estimating poverty rates for small areas for the region of Andalusia (Spain). [ABSTRACT FROM AUTHOR]

Background: 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. Methods and findings: 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. Conclusions: 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. Trial registration: ClinicalTrials.gov NCT03814915. In a modelling study, Naeimeh Atabaki-Pasdar and colleagues apply machine learning techniques to develop models to predict non-alcoholic fatty liver disease diagnosis using multi-omic and clinical data from individuals with and without type 2 diabetes in the IMI DIRECT cohorts. Author summary: Why was this study done?: Globally, about 1 in 4 adults have non-alcoholic fatty liver disease (NAFLD), which adversely affects energy homeostasis (in particular blood glucose concentrations), blood detoxification, drug metabolism, and food digestion. Although numerous noninvasive tests to detect NAFLD exist, these typically include inaccurate blood-marker tests or expensive imaging methods. The purpose of this work was to develop accurate noninvasive methods to aid in the clinical prediction of NAFLD. What did the researchers do and find?: The analyses applied machine learning methods to data from the deep-phenotyped IMI DIRECT cohorts (n = 1,514) to identify sets of highly informative variables for the prediction of NAFLD. The criterion measure was liver fat quantified from MRI. We developed a total of 18 prediction models that ranged from very inexpensive models of modest accuracy to more expensive biochemistry- and/or omics-based models with high accuracy. We found that models using measures commonly collected in either clinical settings or research studies proved adequate for the prediction of NAFLD. The addition of detailed omics data significantly improved the predictive utility of these models. We also found that of all omics markers, proteomic markers yielded the highest predictive accuracy when appropriately combined. What do these findings mean?: We envisage that these new approaches to predicting fatty liver may be of clinical value when screening at-risk populations for NAFLD. The identification of specific molecular features that underlie the development of NAFLD provides novel insights into the disease's etiology, which may lead to the development of new treatments. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). Methods: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6–24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. Results: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. Conclusions/interpretation: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes. [ABSTRACT FROM AUTHOR]

During the last two decades, the Spanish economy has been growing both in terms of employment and economic activity, and the spatial distribution of both is very uneven. At a high level of disaggregation, when observing figures of local employment growth we find major changes between 1991 and 2001 and 2001-2011 decades. The aim of this paper is to provide a better understanding of the main determinants of local employment growth in Spain, including socio-economic factors as well as the structural characteristics of the area. Using spatial auto-regressive models combined with incremental distances to different population size tiers, we account for the spatial relationship between areas and for the influence of the urban hierarchy. Our spatial units of analysis are Local Labor Markets, since they comprise workers and work places within the same functional region. Our results for Spain confirm how sensitive the empirical analysis is and how the direct and/or indirect effect of some structural and socioeconomic factors over local employment growth may vary along time. The two decades under study reflect two very different patterns of local growth, supporting the idea of designing a la carte local policies, which should be reassessed timely. [ABSTRACT FROM AUTHOR]

Abstract: Spain is an ageing country, and the present demographic burden is not homogeneously distributed across space. Will the aged population be evenly distributed in the future, or will disparities broaden over time? Identifying the spatial patterns of the aged population concentration and the existence of a demographic burden convergence/divergence process is a pertinent question in Spain after the regional devolution the country has undergone in the last decades and the coexistence of different regimes. In this paper, we use a non‐parametric approach (geographically weighted regressions) to identify the determinants of the ageing dynamics, checking for the existence of a convergence/divergence ageing process after controlling for the socio‐economic characteristics of the Spanish municipalities. Although global estimations support the convergence hypotheses posed, GWR results show a significant variability of the effects depending on the area considered, which calls for a careful treatment of the results both for analysis and policy purposes. [ABSTRACT FROM AUTHOR]

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2. [ABSTRACT FROM AUTHOR]

Formal modeling of local population growth has usually tended to focus on identifying patterns that are presumed to hold universally. However, as Glaeser, Ponzetto, and Tobio highlighted, these laws are reliable for long-term dynamics; but in some moments or for some places, the balance between the different factors may change, giving rise to different specific behaviors. In this article, we study local population growth in Spain with no intention of searching for universal patterns. Rather, we are interested in identifying how relevant the temporal and spatial heterogeneity may be, that is, to assess the even and uneven effects that population growth determinants can exert across time and space. The geographically weighted regression (GWR) approach applied in this article for two different decades, 1991–2001 and 2001–2011, captures the spatial heterogeneity. Results on the spatially differentiated population growth factors are compared with the global ordinary least squares (OLS) estimators for both decades. Essential factors in urban and regional economics such as size (initial population) or distance (either to the big cities or to the coast) can have different effects on population growth across both space and time, corresponding to the global estimated effects for some areas but diverging from these in others. Using GWR estimation procedures, we can identify changes in the sign or in the intensity of a factor’s effect across space, such that some factors could enhance population growth in one place but reduce it in another. Only after all spatially differentiated local effects have been analyzed and taken into consideration can appropriate national or regional policies be designed à la carte to promote, retain, or deter population growth. [ABSTRACT FROM AUTHOR]

To elucidate the proteomic features of aging in plasma, the subproteome targeted by the SOMAscan assay was profiled in blood samples from 202 females from the TwinsUK cohort. Findings were replicated in 677 independent individuals from the AddNeuroMed, Alzheimer's Research UK, and Dementia Case Registry cohorts. Results were further validated using RNAseq data from whole blood in TwinsUK and the most significant proteins were tested for association with aging-related phenotypes after adjustment for age. Eleven proteins were associated with chronological age and were replicated at protein level in an independent population. These were further investigated at gene expression level in 384 females from the TwinsUK cohort. The two most strongly associated proteins were chordin-like protein 1 (meta-analysis β [SE] = 0.013 [0.001], p = 3.66 × 10(-46)) and pleiotrophin (0.012 [0.005], p = 3.88 × 10(-41)). Chordin-like protein 1 was also significantly correlated with birthweight (0.06 [0.02], p = 0.005) and with the individual Framingham 10-years cardiovascular risk scores in TwinsUK (0.71 [0.18], p = 9.9 × 10(-5)). Pleiotrophin is a secreted growth factor with a plethora of functions in multiple tissues and known to be a marker for cardiovascular risk and osteoporosis. Our study highlights the importance of proteomics to identify some molecular mechanisms involved in human health and aging. [ABSTRACT FROM AUTHOR]

Understanding the genetic architecture of gene expression is an intermediate step in understanding the genetic architecture of complex diseases. RNA sequencing technologies have improved the quantification of gene expression and allow measurement of allele-specific expression (ASE). ASE is hypothesized to result from the direct effect of cis regulatory variants, but a proper estimation of the causes of ASE has not been performed thus far. In this study, we take advantage of a sample of twins to measure the relative contributions of genetic and environmental effects to ASE, and we find substantial effects from gene × gene (G×G) and gene × environment (G×E) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but where the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants. [ABSTRACT FROM AUTHOR]

Even though there is a general consensus in the regional economics literature about the relevance of agglomeration economies in the distribution of economic activity, analyses of regional differences in employability in Spain have found limited empirical evidence about the role played by population concentrations. Our hypothesis in this paper is that the lack of evidence supporting theoretically expected behavior about the role of agglomerations is mainly explained by the fact that administrative rather analytical regions have been used. To check this, we propose a study based on spatially disaggregated data from the Population Census and its aggregation into analytical regions that incorporate ideas from the new economic geography. Our results regarding the effect of space on employment opportunities with this alternative classification suggest, as expected, that living in large cities or close to metropolitan areas increases the possibilities of being employed. The different results support the need for more highly disaggregated data at spatial level in order to overcome the limitations inherent to empirical analysis based on administrative regions. [ABSTRACT FROM AUTHOR]

Background: Although a large body of literature is available that describes the effects of smoking, asthma and COPD on lung function, most studies are restricted to a small age range and to one factor. As a consequence, available results are incomplete and often difficult to compare, also due to the ways the effects are expressed. Furthermore, current approaches consider one type of measurement only or several types separately. Methods: We propose a probabilistic model that expresses the effects as number of years added to chronological age or, in other words, that estimates the biological age of the lungs. Using biological age as a measure of the effects has the advantage of facilitating the understanding of their severity and comparison of results. In our model, chronological age and other factors affecting the health status of the lungs generate biological age, which in turn generates lung function measurements. This structure enables the use of multiple types of measurement to obtain a more precise estimate of the effects and parameter sharing for characterization over large age ranges and of co-occurrence of factors with little data. We treat the parameters that model smoking habits and lung diseases as random variables to obtain uncertainty in the estimated effects. Results: We use the model to investigate the effects of smoking, asthma and COPD on the TwinsUK Registry. Our results suggest that the combination of smoking with lung disease(s) has higher effect than smoking or lung disease(s) alone, and that, in smokers, co-occurrence of asthma and COPD is more detrimental than asthma or COPD alone. Conclusions: The proposed model or other models based on a similar approach could be of help in improving the understanding of factors affecting lung function by enabling characterizations over large age ranges and of co-occurrence of factors with little data and the use of multiple types of measurement. The software implementing the model can be downloaded at the first author's webpage. [ABSTRACT FROM AUTHOR]

Copyright of Jahrbuch fur Regionalwissenschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although organophosphorus pesticides (OP) share a common mode of action, there is increased awareness that they elicit a diverse range of gene expression responses. As yet however, there is no clear understanding of these responses and how they interact with ambient environmental conditions. In the present study, we investigated genome-wide gene expression profiles in the nematode Caenorhabditis elegans exposed to two OP, chlorpyrifos and diazinon, in single and combined treatments at different temperatures. Our results show that chlorpyrifos and diazinon induced expression of different genes and that temperature affected the response of detoxification genes to the pesticides. The analysis of transcriptional responses to a combination of chlorpyrifos and diazinon shows interactions between toxicants that affect gene expression. Furthermore, our combined analysis of the transcriptional responses to OP at different temperatures suggests that the combination of OP and high temperatures affect detoxification genes and modified the toxic levels of the pesticides. [ABSTRACT FROM AUTHOR]

Organophosphorus pesticides (OPs) were originally designed to affect the nervous system by inhibiting the enzyme acetylcholinesterase, an important regulator of the neurotransmitter acetylcholine. Over the past years evidence is mounting that these compounds affect many other processes. Little is known, however, about gene expression responses against OPs in the nematode Caenorhabditis elegans. This is surprising because C. elegans is extensively used as a model species in toxicity studies. To address this question we performed a microarray study in C. elegans which was exposed for 72 hrs to two widely used Ops, chlorpyrifos and diazinon, and a low dose mixture of these two compounds. Our analysis revealed transcriptional responses related to detoxification, stress, innate immunity, and transport and metabolism of lipids in all treatments. We found that for both compounds as well as in the mixture, these processes were regulated by different gene transcripts. Our results illustrate intense, and unexpected crosstalk between gene pathways in response to chlorpyrifos and diazinon in C. elegans. [ABSTRACT FROM AUTHOR]

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues. Mechanistic inference following GWAS is hampered by the lack of tissue-specific transcriptomic resources. Here the authors combine genetic variants predisposing to type 2 diabetes with human pancreatic islet RNA-seq data. They identify 7741 islet expression quantitative trait loci (eQTLs), providing a resource for functional interpretation of association signals mapping to non-coding sequence. [ABSTRACT FROM AUTHOR]

Background: 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.Methods and Findings: 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.Conclusions: 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.Trial Registration: ClinicalTrials.gov NCT03814915. [ABSTRACT FROM AUTHOR]

Roux-en-Y gastric bypass surgery (RYGB) has been effective for inducing weight-loss and remission of diabetes in obese persons. Nonetheless, the response to RYGB and resulting improvements in glycemic control are heterogeneous and not well understood. To gain molecular insights into how individuals respond to RYGB, we monitored the longitudinal effects of RYGB surgery via circulating proteins. We quantified 368 proteins using multiplexed and sensitive immunoassays (Olink) in sera collected from 146 obese persons with T2D (BMI > 35 kg/m2) prior to, and at one and three months after surgery. We observed an overall longitudinal change in circulating levels for ∼50% of the proteins (FDR < 0.01). This included a post-RYGB increase in levels of GH, IGFBP-2, NT-proBNP, REGA1 and MMP-3, as well as a decrease in levels of SSC4D, SERPINA12, GIF, FBP1, CES1. To deconvolute the inter-individual heterogeneity of the circulating proteomes and protein-related longitudinal dynamics, we clustered the total of ∼44,000 protein profiles into 10 response patterns. Just 8% (28/368) of the proteins' levels changed in a common manner across the majority of individuals, as these were grouped into 3 or fewer clusters. Among these were proteins related to adipogenesis (LEP, DLK1, GH), white adipocyte differentiation (LEP, FABP4), as well as innate immunity (CHIT1, PTX3, CSTB). However, out of these 28 proteins, only levels of VSIG2 were significantly associated (FDR < 0.01) with diabetes remission at 12 months (HbA1c < 6.5%, no diabetes medication). Overall, our results suggest a wide-reaching, individual-specific, but predominantly short-term impact of RYGB surgery on the circulating proteome. Current investigations examine how genetic variance and mRNA expression in adipose and liver tissue influence the donors' circulating proteomes in order to explain the observed heterogeneity and its impact on a person's molecular homeostasis. Disclosure: C.E. Thomas: None. R.S. Häussler: None. M. Hong: None. V. Raverdy: None. M. Dale: None. M. Canouil: None. G. Mazzoni: None. A. Viñuela: None. P. Froguel: None. S. Brunak: Board Member; Self; Intomics A/S, Proscion A/S. Research Support; Self; Novo Nordisk Foundation. Stock/Shareholder; Self; Lundbeck, Novo Nordisk A/S. J.M. Schwenk: None. Funding: Innovative Medicines Initiative Joint Undertaking (115317); European Union's Seventh Framework Programme (FP7/2007-2013); Conseil Regional Nord-Pas-de-Calais; European Commission (FEDER 12003944); European Genomic Institute for Diabetes (ANR-10-LABX-46); Foundation Coeur et Arteres (R15112EE); Fonds hospitalier d'aide a l'emergence et a la structuration des activities et des equipes de recherche 2015 (CHRU Lille, France) [ABSTRACT FROM AUTHOR]

The original version of this article unfortunately contained a mistake. The maps on the right-hand side in Fig. 1 have the wrong titles. NUTS 2 regions should be Local Labour Markets. [ABSTRACT FROM AUTHOR]

In the 2000s, and with natural population growth rates close to zero, Spain experienced an inflow of almost 5 million immigrants. These new Spanish residents did not tend to locate evenly across the territory and contributed to putting pressure on the already large spatial population imbalances between cities and rural areas, between the coast and the interior and between the central and peripheral areas. Covering the whole Spanish territory and using the 804 Spanish local labour markets as units of analysis, the objective of this paper is to analyse the local determinants of the attractiveness of a place for immigrants. The estimation of geographically weighted regressions (GWR) shows the necessity of including spatial heterogeneity in the analysis, as the effect of the traditional factors explaining immigrant concentration processes—such as job‐related characteristics or the networks established by previous immigrants—exert different and even contrary effects across the Spanish territory. Although global estimations would reject the significance of agglomeration economies or the relative location of a place, the adoption of the GWR approach shows that these regional economic factors are key to understanding the geographical concentration of immigrants in Spain but show different—even contrary—responses across space. Spatial heterogeneity supports the idea that any national/regional policy to enhance (or contain) immigrant concentration should be designed a la carte and be implemented at the local level. [ABSTRACT FROM AUTHOR]

Plasma proteins can provide valuable insights on human health and disease states. Within the framework of the EU IMI project DIRECT (https://www.direct-diabetes.org), we used a set of affinity proteomic methods to profile > 3100 study participants at baseline. Multiplexed assays quantified more than 600 unique proteins in EDTA plasma from this multi-center cohort that included 2300 subjects at risk of developing T2D (HbA1c ~ 6-6.5%) as well as 800 with early T2D (HbA1c > 6.5%). Using extensive clinical and other omics metadata available, the aim of the investigation was to identify plasma proteins associated with baseline traits. An initial analysis highlighted the importance of considering sample-related and pre-analytical variables as possible confounders in the data analysis. Hence, we used linear mixed models that included several parameters such as age, sex, study center and collection date. Next, we defined proteins associating with any of the >50 quantitative clinical traits at baseline. We found more than 300 proteins in plasma that were associated with diabetes related traits (adjusted p-value < 0.0001), many of which were prominently associated with BMI. The shortlisted candidates included leptin which associates with waist circumference and BMI; IGFBP1 and IGFBP2 to Matsuda; adiponectin to basal insulin secretion rate and fasting HDL; LDL receptor proteins to fasting triglycerides; APOM to fasting cholesterol; or IL8 and MCP-1 to fasting AST. In addition, we performed pQTL analysis to assess any connection between the protein values in plasma and genetic variants. We observed >400 cis-pQTLs (q-value < 0.05), such as for APOM (rs2736163, p = 5.15 e-24), which illustrated that many of the studied protein profiles are affected by a genetic component. With follow-up samples collected 3-4 years after starting the study, the baseline values will serve as valuable indicators of progression and allow study of how each participant's disease phenotype changes over time or due to treatment. Disclosure: M. Hong: None. A. Viñuela: None. R.S. Häussler: None. M. Dale: None. R.W. Koivula: None. J. Fernandez-Tajes: None. A. Mahajan: None. R. Bizzotto: Research Support; Self; GlaxoSmithKline plc. A. Mari: Consultant; Self; Eli Lilly and Company. Research Support; Self; Boehringer Ingelheim International GmbH. E. Dermitzakis: Advisory Panel; Self; DNAnexus LTD. Board Member; Self; Hybridstat LTD. M. McCarthy: Advisory Panel; Self; European Association for the Study of Diabetes, Pfizer Inc. Consultant; Self; Eli Lilly and Company, Merck & Co., Inc. Consultant; Spouse/Partner; Merck & Co., Inc. Research Support; Self; AbbVie Inc., Boehringer Ingelheim International GmbH. Research Support; Spouse/Partner; Diabetes UK. Research Support; Self; Janssen Pharmaceuticals, Inc., Merck & Co., Inc., National Institutes of Health. Research Support; Spouse/Partner; National Institutes of Health. Research Support; Self; Novo Nordisk A/S. Research Support; Spouse/Partner; Novo Nordisk A/S. Research Support; Self; Novo Nordisk Foundation, Roche Pharma, Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. P.W. Franks: Board Member; Self; Zoe Ltd. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk A/S, Novo Nordisk Foundation, Sanofi, Servier. E. Pearson: None. J.M. Schwenk: None. Funding: Innovative Medicines Initiative Joint Undertaking (115317) [ABSTRACT FROM AUTHOR]

Background: Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear. Methods: We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat. Results: We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation. Conclusions: Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung. [ABSTRACT FROM AUTHOR]

Background: Although a large body of literature is available that describes the effects of smoking, asthma and COPD on lung function, most studies are restricted to a small age range and to one factor. As a consequence, available results are incomplete and often difficult to compare, also due to the ways the effects are expressed. Furthermore, current approaches consider one type of measurement only or several types separately.Methods: We propose a probabilistic model that expresses the effects as number of years added to chronological age or, in other words, that estimates the biological age of the lungs. Using biological age as a measure of the effects has the advantage of facilitating the understanding of their severity and comparison of results. In our model, chronological age and other factors affecting the health status of the lungs generate biological age, which in turn generates lung function measurements. This structure enables the use of multiple types of measurement to obtain a more precise estimate of the effects and parameter sharing for characterization over large age ranges and of co-occurrence of factors with little data. We treat the parameters that model smoking habits and lung diseases as random variables to obtain uncertainty in the estimated effects.Results: We use the model to investigate the effects of smoking, asthma and COPD on the TwinsUK Registry. Our results suggest that the combination of smoking with lung disease(s) has higher effect than smoking or lung disease(s) alone, and that in smokers, co-occurrence of asthma and COPD is more detrimental than asthma or COPD alone.Conclusions: The proposed model or other models based on a similar approach could be of help in improving the understanding of factors affecting lung function by enabling characterizations over large age ranges and of co-occurrence of factors with little data and the use of multiple types of measurement. The software implementing the model can be downloaded at the first author's webpage. [ABSTRACT FROM AUTHOR]