Notice bibliographique
Résumé
Personalized/precision medicine is forthcoming on the path of Medicine from art to science. Hippocrates stated that ‘it is more relevant to know which person has the disease than to know which disease the person has’. In reality, we are just beginning to have access, mostly because of applications of ‘Omics’, starting with ‘Genomics’, to tools accurate or ‘precise’ enough to accomplish such personalization. Many definitions of personalized medicine exist and most of them omit the interactions between genetic and environmental factors with one notable exception, the National Institutes of Health Cancer Institute that defines personalized medicine as ‘…a form of healthcare that considers information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease’. Precision medicine can perhaps do without environment, but personalized medicine without consideration of environment, a person's exposure to it or adapting to it, cannot function as our capacity to react, adapt, and control is driven by our inherited as well as cultural capacity to cope with it. Topol [1], who coined the term ‘individualized medicine’, nicely summarized the evidence that it is there from ‘Prewomb to Tomb’, encompassing prenatal planning, conception, 9-month development in utero, monogenic diseases of childhood, genetic susceptibility to infectious diseases followed later by complex diseases with an increasing impact of environmental exposure, determinants of healthy or not ageing and finally death. Although geneticists generally accept that the environment plays a role in complex diseases and epidemiologists are now keener to accept that genetic factors play a role in common diseases, the respective roles of genetic and environmental factors and the mechanisms of their interactions are still not completely understood. We have recently reviewed and explored how concepts such as the impact of genes on early penetrance, the heterogeneity of complex traits and the effect of varying physiological conditions can affect our capacity to detect G × E interactions [2]. At the other end of the spectrum of life is yet another type of exposome, affecting sperm maturation, fecundation, and foetal development. All are affected by both maternal and paternal imprinting. One major regulator of these events is epigenetics; DNA modification without sequence change. This relatively new and fast growing field of research has been scholarly reviewed in this issue of Journal of Hypertension by Li from Hocher's group [3] who analysed specifically paternal programming that leads to cardiometabolic disorders later in life. The focus on potential mechanisms underlying the impact of early life exposure on later disease is timely as we know the phenomenon for a relatively long time from Barker's [4] hypothesis but its mechanisms remained elusive until recently. The review focuses on paternal programming in relation to obesity and diabetes. It covers epidemiological evidence presenting convincing data supporting the contribution of paternal transmission. It is recognized that epidemiological evidence in itself cannot distinguish between transmissions of ‘obesity genes’ versus environmentally modified epigenetic transmission from the father's sperm. This is a specific role of studies in experimental animal models that can provide mechanistic evidence for paternal transmissions. Several examples are discussed, most clear-cut are the experiments with transgenerational transmission of insulin levels and secretion from father with nongenetic, stretozotocin-induced diabetes. The evidence in this case is complete with demonstration of epigenetic mechanisms, increased methylation of such genes as Igf2 and H19 resulting in modified transcription leading to abnormalities in Langerhans Islets. Additional examples are consequences of exposure of paternal grandfathers from generation F0 to different diets, adverse stimuli, or diseases such as diabetes to F1 and F2 progenies. Noticeably, the transmission of some of the traits to the F2 generation was more pronounced for maternal F1 lines compared with the paternal ones, yet the evidence for the existence of paternal transmission is convincing. The most highlighting part is the section that describes the mechanisms of epigenetic control of parental programming. What may be novel to some, is the fact that while it is generally recognized that most of methylated DNA is ‘cleaned’ prior to fecundation, some of it is resistant to DNA demethylation and is transmitted to the next generations by environmentally impacted genetic functionality, without sequence change roles of perm RNA. Histone acetylation are well discussed as additional mechanisms. Somewhat less attention is paid to the importance of the ‘genetic background’ as a modulator of epigenetic regulation. Admittedly, the area did not receive all attention it deserves. Specifically in hypertension where these studies are feasible as a plethora of congenic, recombinant inbred or transgenic rodent strains exist. They were used to demonstrate the involvement of many genomic factors on the ontogenesis of hypertension. It is possible to evaluate neonates of the F2 generation that are genetically identical to their parent and grandparent counterparts as recombinant inbred strains have been developed by Dumas et al. [5] in Prague and found to be useful for such studies [6]. The impact of environmental intervention during pregnancy and lactation was determined [7]. The review in this issue of Journal of Hypertension lays the ground for further work and is useful to situate the current knowledge and understanding to what remains to be uncovered. One of the future tasks could be to identify the genomic determinants of interactions with environment particularly when influencing the development of offspring, determine their future susceptibility to disease and capacity to adapt to life. The consequence for public health to integrate environmental, epigenetic, and genetic efforts for holistic understanding is enormous. Preventive interventions require the power to predict and full mechanistic understanding of environment and genetic interactions, and their relative importance is necessary to further stratify our therapeutic targets and tools. ACKNOWLEDGEMENTS P.H. wishes to thank Professor Johanne Tremblay for comments and fruitful discussions. Conflicts of interest There are no conflicts of interest.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».