Invest Ophthalmol Vis Sci

Purpose: We investigated the cross-sectional associations between macular pigment optical density (MPOD), plasma lutein (L), and zeaxanthin (Z) concentrations and cognitive function in 184 older adults of the 3-City-Bordeaux cohort. Methods: MPOD was measured using the two-wavelength autofluorescence method with a modified scanning laser ophthalmoscope. Plasma L and Z (L+Z) concentrations were determined by high-performance liquid chromatography and were considered either crude or expressed as a ratio of the concentration of plasma lipids (total cholesterol [TC] + triglycerides [TG]). Cognitive performances were assessed using the following four separate neuropsychological tests: the Mini-Mental State Examination (MMSE), the Isaacs Set Test (IST), the Benton Visual Retention Test (BVRT), and the sum of the three free recalls of the Free and Cued Selective Reminding Test (FCSRT). These test results were summarized by a composite global cognitive z-score. Results: Higher MPOD at 0.5 degrees was significantly associated with a higher composite z-score (beta = 0.15, 95% confidence interval [CI] 0.04-0.26), higher BVRT (beta = 0.39, 95%CI 0.08-0.70), and higher IST (beta = 1.16, 95%CI 0.11-2.22) performances. Higher plasma L+Z concentrations were significantly associated with higher IST scores (beta = 0.97, 95%CI 0.01-1.94). Furthermore, a higher L+Z/TC+TG ratio was associated with a higher composite z-score (beta = 0.12, 95%CI 0.01-0.23), along with higher IST (beta = 1.02, 95%CI 0.002-2.04) and FCSRT (beta = 1.55, 95%CI 0.41-2.69) performances. Conclusions: This analysis suggested that both higher MPOD and L+Z concentrations were significantly associated with higher cognitive performances. However, MPOD measurements have the advantage of being a fast and representative measure of long-term carotenoid intake

Acta Ophthalmol

OBJECTIVE: This study aimed to develop a deep learning (DL) model, named 'DeepAlienorNet', to automatically extract clinical signs of age-related macular degeneration (AMD) from colour fundus photography (CFP). METHODS AND ANALYSIS: The ALIENOR Study is a cohort of French individuals 77 years of age or older. A multi-label DL model was developed to grade the presence of 7 clinical signs: large soft drusen (>125 μm), intermediate soft (63-125 μm), large area of soft drusen (total area >500 μm), presence of central soft drusen (large or intermediate), hyperpigmentation, hypopigmentation, and advanced AMD (defined as neovascular or atrophic AMD). Prediction performances were evaluated using cross-validation and the expert human interpretation of the clinical signs as the ground truth. RESULTS: A total of 1178 images were included in the study. Averaging the 7 clinical signs' detection performances, DeepAlienorNet achieved an overall sensitivity, specificity, and AUROC of 0.77, 0.83, and 0.87, respectively. The model demonstrated particularly strong performance in predicting advanced AMD and large areas of soft drusen. It can also generate heatmaps, highlighting the relevant image areas for interpretation. CONCLUSION: DeepAlienorNet demonstrates promising performance in automatically identifying clinical signs of AMD from CFP, offering several notable advantages. Its high interpretability reduces the black box effect, addressing ethical concerns. Additionally, the model can be easily integrated to automate well-established and validated AMD progression scores, and the user-friendly interface further enhances its usability. The main value of DeepAlienorNet lies in its ability to assist in precise severity scoring for further adapted AMD management, all while preserving interpretability.

Obesity in French Inmates: Gender Differences and Relationship with Mood, Eating Behavior and Physical Activity

CONTEXT: Inmates, notably women, are at greater risk for obesity and metabolic complications than the general population according to several studies from high income countries. Data regarding French correctional institutions are lacking so far. To fill this gap, we have assessed in a sample from a French prison (33 females and 18 males) the gender-specific effect of incarceration on weight and body mass index (BMI) and examined their current metabolic status. Furthermore, to reveal the possible determinants of increased obesity, we analyzed emotional vulnerability, eating behavior and physical activity using self-reported questionnaires. RESULTS: In this sample, obesity (BMI≥30 kg/m2) was already frequent in women (18.2%) but rather scarce for men (11%) at prison entry. Incarceration worsened the rate of obesity in both genders (21.2% and 16.7% respectively). At the time of study, abdominal obesity estimated through waist circumference was particularly prevalent in women (69.7%) versus men (27.8%) and metabolic syndrome was detected in 33% of female against none in male inmates. Abdominal obesity was associated with female sex (p<0.03), low physical activity (p<0.05) and eating disorder (p = 0.07) in univariate analyses. Low physical activity remained significant as an explanatory factor of higher abdominal obesity in multivariate analysis. A marked difference between genders was found for practice of physical activity with a higher proportion of women compared to men being inactive (37.9% vs. 11.8%) and fewer women being very active (17.2% vs. 41.2%). CONCLUSION: This study revealed that a significant proportion of women of this correctional institution combined established obesity, a metabolic syndrome and very little practice of physical activity which put them at high risk of cardiovascular disease. Thus, obesity should be better surveyed and treated in prison, especially for female inmates. Increased physical activity, adapted to obese women, would be the first mean to decrease obesity and gender differences

PLoS One

Age-related macular degeneration (AMD) is a common, progressive multifactorial vision-threatening disease and many genetic and environmental risk factors have been identified. The risk of AMD is influenced by lifestyle and diet, which may be reflected by an altered metabolic profile. Therefore, measurements of metabolites could identify biomarkers for AMD, and could aid in identifying high-risk individuals. Hypothesis-free technologies such as metabolomics have a great potential to uncover biomarkers or pathways that contribute to disease pathophysiology. To date, only a limited number of metabolomic studies have been performed in AMD. Here, we aim to contribute to the discovery of novel biomarkers and metabolic pathways for AMD using a targeted metabolomics approach of 188 metabolites. This study focuses on non-advanced AMD, since there is a need for biomarkers for the early stages of disease before severe visual loss has occurred. Targeted metabolomics was performed in 72 patients with early or intermediate AMD and 72 control individuals, and metabolites predictive for AMD were identified by a sparse partial least squares discriminant analysis. In our cohort, we identified four metabolite variables that were most predictive for early and intermediate stages of AMD. Increased glutamine and phosphatidylcholine diacyl C28:1 levels were detected in non-advanced AMD cases compared to controls, while the rate of glutaminolysis and the glutamine to glutamate ratio were reduced in non-advanced AMD. The association of glutamine with non-advanced AMD corroborates a recent report demonstrating an elevated glutamine level in early AMD using a different metabolomics technique. In conclusion, this study indicates that metabolomics is a suitable method for the discovery of biomarker candidates for AMD. In the future, larger metabolomics studies could add to the discovery of novel biomarkers in yet unknown AMD pathways and expand our insights in AMD pathophysiology

Ophthalmology

OBJECTIVE: In the current study we aimed to identify metabolites associated with age-related macular degeneration (AMD) by performing the largest metabolome association analysis in AMD to date. In addition, we aimed to determine the effect of AMD-associated genetic variants on metabolite levels, and aimed to investigate associations between the identified metabolites and activity of the complement system, one of the main AMD-associated disease pathways. DESIGN: Case-control assocation analysis of metabolomics data. SUBJECTS: 2,267 AMD cases and 4,266 controls from five European cohorts. METHODS: Metabolomics was performed using a high-throughput H-NMR metabolomics platform, which allows the quantification of 146 metabolite measurements and 79 derivative values. Metabolome-AMD associations were studied using univariate logistic regression analyses. The effect of 52 AMD-associated genetic variants on the identified metabolites was investigated using linear regression. In addition, associations between the identified metabolites and activity of the complement pathway (defined by the C3d/C3 ratio) were investigated using linear regression. MAIN OUTCOME MEASURES: Metabolites associated with AMD RESULTS: We identified 60 metabolites that were significantly associated with AMD, including increased levels of large and extra-large HDL subclasses and decreased levels of VLDL, amino acids and citrate. Out of 52 AMD-associated genetic variants, seven variants were significantly associated with 34 of the identified metabolites. The strongest associations were identified for genetic variants located in or near genes involved in lipid metabolism (ABCA1, CETP, APOE, LIPC) with metabolites belonging to the large and extra-large HDL subclasses. In addition, 57 out of 60 metabolites were significantly associated with complement activation levels, and these associations were independent of AMD status. Increased large and extra-large HDL levels and decreased VLDL and amino acid levels were associated with increased complement activation. CONCLUSIONS: Lipoprotein levels were associated with AMD-associated genetic variants, while decreased essential amino acids may point to nutritional deficiencies in AMD. We observed strong associations between the vast majority of the AMD-associated metabolites and systemic complement activation levels, independent of AMD status. This may indicate biological interactions between the main AMD disease pathways, and suggests that multiple pathways may need to be targeted simultaneously for successful treatment of AMD

Prevalence of Age-Related Macular Degeneration in Europe: The Past and the Future

Purpose Age-related macular degeneration (AMD) is a frequent, complex disorder in elderly of European ancestry. Risk profiles and treatment options have changed considerably over the years, which may have affected disease prevalence and outcome. We determined the prevalence of early and late AMD in Europe from 1990 to 2013 using the European Eye Epidemiology (E3) consortium, and made projections for the future. Design Meta-analysis of prevalence data. Participants A total of 42 080 individuals 40 years of age and older participating in 14 population-based cohorts from 10 countries in Europe. Methods AMD was diagnosed based on fundus photographs using the Rotterdam Classification. Prevalence of early and late AMD was calculated using random-effects meta-analysis stratified for age, birth cohort, gender, geographic region, and time period of the study. Best-corrected visual acuity (BCVA) was compared between late AMD subtypes; geographic atrophy (GA) and choroidal neovascularization (CNV). Main Outcome Measures Prevalence of early and late AMD, BCVA, and number of AMD cases. Results Prevalence of early AMD increased from 3.5% (95% confidence interval [CI] 2.1%–5.0%) in those aged 55–59 years to 17.6% (95%

Prédiction du risque de DMLA : identification de nouveaux biomarqueurs et modélisation du risque

Age-related macular degeneration (AMD) is the leading cause of blindness in industrialized countries. AMD is a complex and multifactorial disease with major consequences on the quality of life. Numerous genetic and non-genetic risk factors play an important role in the pathogenesis of the advanced stages of AMD. Existing prediction models rely on a restricted set of risk factors and are still not widely used in the clinical routine.The first objective of this work was to identify new circulating biomarkers of AMD’s risk using a lipidomics approach. Based on a post-mortem study, we identified the most predictive circulating lipids of retinal content in omega-3 polyunsaturated fatty acids (w-3 PUFAs). We combined penalization and dimension reduction to establish a prediction model based on plasma concentration of 7 cholesteryl ester species. We further validated this model on case-control and interventional studies. These biomarkers could help identify individuals at high risk of AMD who could be supplemented with w-3 PUFAs.The second objective of this thesis was to develop a prediction model for advanced AMD. This model incorporated a wide set of phenotypic, genotypic and lifestyle risk factors. An originality of our work was to use a penalized regression method – a machine learning algorithm – in a survival framework to handle multicollinearities among the risk factors. We also accounted for interval censoring and the competing risk of death by using an illness-death model. Our model was then validated on an independent population-based cohort.It would be interesting to integrate the circulating biomarkers identified in the lipidomics study to our prediction model and to further validate it on other external cohorts. This prediction model can be used for patient selection in clinical trials to increase their efficiency and paves the way towards making precision medicine for AMD patients a reality in the near future.La dégénérescence maculaire liée à l’âge (DMLA) est la première cause de cécité dans les pays industrialisés. C’est une maladie complexe et multifactorielle ayant des conséquences majeures sur la qualité de vie des personnes atteintes. De nombreux facteurs de risque, génétiques et non génétiques, jouent un rôle important dans la pathogénèse des stades avancés de la DMLA. Les modèles de prédiction développés à ce jour reposent sur un nombre limité de ces facteurs, et sont encore peu utilisés dans la pratique clinique.Ce travail de thèse avait pour premier objectif d’identifier de nouveaux biomarqueurs circulants du risque de DMLA. Ainsi, à partir d’une étude post-mortem basée sur une approche de lipidomique, nous avons identifié les composés lipidiques sanguins les plus prédictifs des concentrations rétiniennes en acides gras polyinsaturés omégas 3 (AGPI w-3). Nous avons développé un modèle de prédiction basé sur 7 espèces de lipides des esters de cholestérol. Ce modèle, obtenu en combinant pénalisation et réduction de la dimension, a ensuite été validé dans des études cas-témoins de DMLA et dans un essai clinique randomisé de supplémentation en AGPI w-3. Ces biomarqueurs pourraient être utiles pour l’identification des personnes à haut risque de DMLA, qui pourraient ainsi bénéficier d’une supplémentation en AGPI w-3.Le deuxième objectif de cette thèse était de développer un modèle de prédiction du risque de progression vers une DMLA avancée à partir de facteurs de risque génétiques, phénotypiques et environnementaux. Une originalité de notre travail a été d’utiliser une méthode de régression pénalisée – un algorithme d’apprentissage automatique – dans un cadre de survie afin de tenir compte de la multicollinéarité entre les facteurs de risque. Nous avons également pris en compte la censure par intervalle et le risque compétitif du décès via un modèle à 3 états sain-malade-mort. Nous avons ensuite validé ce modèle sur une étude indépendante en population générale.Il serait intéressant de valider ce modèle de prédiction dans d’autres études indépendantes en y incluant les biomarqueurs circulants identifiés à partir de l’étude de lipidomique effectuée dans le cadre de cette thèse. Le but final serait d’intégrer cet outil prédictif dans la pratique clinique afin de rendre la médecine de précision une réalité pour les patients atteints de DMLA dans le futur proche

AMD risk prediction : identification of new biomarkers and risk modeling

La dégénérescence maculaire liée à l’âge (DMLA) est la première cause de cécité dans les pays industrialisés. C’est une maladie complexe et multifactorielle ayant des conséquences majeures sur la qualité de vie des personnes atteintes. De nombreux facteurs de risque, génétiques et non génétiques, jouent un rôle important dans la pathogénèse des stades avancés de la DMLA. Les modèles de prédiction développés à ce jour reposent sur un nombre limité de ces facteurs, et sont encore peu utilisés dans la pratique clinique.Ce travail de thèse avait pour premier objectif d’identifier de nouveaux biomarqueurs circulants du risque de DMLA. Ainsi, à partir d’une étude post-mortem basée sur une approche de lipidomique, nous avons identifié les composés lipidiques sanguins les plus prédictifs des concentrations rétiniennes en acides gras polyinsaturés omégas 3 (AGPI w-3). Nous avons développé un modèle de prédiction basé sur 7 espèces de lipides des esters de cholestérol. Ce modèle, obtenu en combinant pénalisation et réduction de la dimension, a ensuite été validé dans des études cas-témoins de DMLA et dans un essai clinique randomisé de supplémentation en AGPI w-3. Ces biomarqueurs pourraient être utiles pour l’identification des personnes à haut risque de DMLA, qui pourraient ainsi bénéficier d’une supplémentation en AGPI w-3.Le deuxième objectif de cette thèse était de développer un modèle de prédiction du risque de progression vers une DMLA avancée à partir de facteurs de risque génétiques, phénotypiques et environnementaux. Une originalité de notre travail a été d’utiliser une méthode de régression pénalisée – un algorithme d’apprentissage automatique – dans un cadre de survie afin de tenir compte de la multicollinéarité entre les facteurs de risque. Nous avons également pris en compte la censure par intervalle et le risque compétitif du décès via un modèle à 3 états sain-malade-mort. Nous avons ensuite validé ce modèle sur une étude indépendante en population générale.Il serait intéressant de valider ce modèle de prédiction dans d’autres études indépendantes en y incluant les biomarqueurs circulants identifiés à partir de l’étude de lipidomique effectuée dans le cadre de cette thèse. Le but final serait d’intégrer cet outil prédictif dans la pratique clinique afin de rendre la médecine de précision une réalité pour les patients atteints de DMLA dans le futur proche.Age-related macular degeneration (AMD) is the leading cause of blindness in industrialized countries. AMD is a complex and multifactorial disease with major consequences on the quality of life. Numerous genetic and non-genetic risk factors play an important role in the pathogenesis of the advanced stages of AMD. Existing prediction models rely on a restricted set of risk factors and are still not widely used in the clinical routine.The first objective of this work was to identify new circulating biomarkers of AMD’s risk using a lipidomics approach. Based on a post-mortem study, we identified the most predictive circulating lipids of retinal content in omega-3 polyunsaturated fatty acids (w-3 PUFAs). We combined penalization and dimension reduction to establish a prediction model based on plasma concentration of 7 cholesteryl ester species. We further validated this model on case-control and interventional studies. These biomarkers could help identify individuals at high risk of AMD who could be supplemented with w-3 PUFAs.The second objective of this thesis was to develop a prediction model for advanced AMD. This model incorporated a wide set of phenotypic, genotypic and lifestyle risk factors. An originality of our work was to use a penalized regression method – a machine learning algorithm – in a survival framework to handle multicollinearities among the risk factors. We also accounted for interval censoring and the competing risk of death by using an illness-death model. Our model was then validated on an independent population-based cohort.It would be interesting to integrate the circulating biomarkers identified in the lipidomics study to our prediction model and to further validate it on other external cohorts. This prediction model can be used for patient selection in clinical trials to increase their efficiency and paves the way towards making precision medicine for AMD patients a reality in the near future

Bioinformatics

MOTIVATION: In some prediction analyses, predictors have a natural grouping structure and selecting predictors accounting for this additional information could be more effective for predicting the outcome accurately. Moreover, in a high dimension low sample size framework, obtaining a good predictive model becomes very challenging. The objective of this work was to investigate the benefits of dimension reduction in penalized regression methods, in terms of prediction performance and variable selection consistency, in high dimension low sample size data. Using two real datasets, we compared the performances of lasso, elastic net, group lasso, sparse group lasso, sparse partial least squares (PLS), group PLS and sparse group PLS. RESULTS: Considering dimension reduction in penalized regression methods improved the prediction accuracy. The sparse group PLS reached the lowest prediction error while consistently selecting a few predictors from a single group. AVAILABILITY AND IMPLEMENTATION: R codes for the prediction methods are freely available at https://github.com/SoufianeAjana/Blisar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online