Bibliographic record
Abstract
Because cardiovascular risk is determined for a great part by genetic factors, there is considerable interest in finding which allelic variants of which genes associate with either improved or worsened cardiovascular prospects. Up until now, most successes in gene identification have been obtained in genes with such strong effects that they affect the distribution of phenotypes within families in a Mendelian fashion. Unfortunately, susceptibility to most common diseases, including cardiovascular ones, is determined by the interactions between a host of genes (each with weak effects on their own) and environmental factors (which modulate the amplitude of the effect of each gene). By contrast to Mendelian traits, gene variants linked to complex traits are much more difficult to identify. Recent efforts have allowed for the identification of thousands of single nucleotide polymorphisms within all human genes, which should make it possible to perform genome-wide association studies. However, until a way is found to perform such studies in a cost-efficient manner, there is still the need to rely on other means to identify candidate genes whose role can be tested in more traditional association studies. Genetic studies with inbred animal models may be particularly valuable in this regard. It is indeed much easier to detect the phenotypic effects of allelic variants within the controlled background of animals derived from well-defined inbred strains. Moreover, these models provide means to test whether there is a mechanistic link between particular gene variants and a phenotype of interest. For example, in mice, it has been shown that inactivation of the genes coding either for the precursors of natriuretic peptides, or for their receptors, leads to the development of mild hypertension and, to a greater extent, of left ventricular hypertophy (LVH) [1–3]. Similarly, in rat models, there is evidence that naturally occurring variants of the natriuretic peptide precursor A (Nppa) gene are associated with both altered expression of atrial natriuretic factor (ANF) within cardiac ventricles and with LVH [4,5]. Similarly, a polymorphism within the Npr1 promoter correlates with diastolic blood pressure and Npr1 mRNA levels in recombinant inbred rats derived from SHR/BN rat crosses [6]. How about humans? In a Japanese human cohort, a polymorphism that decreases the transcriptional activity of Npr1 (the gene coding for the receptor via which ANF activates guanylate cyclase) was more prevalent in a group of patients with essential hypertension and LVH patients than in a control group [7]. To explore further the possible role of natriuretic peptide-mediated signalling, in this issue of the journal, Pitzalis et al. [8] studied a cohort of Italian patients who were tested for associations with variants of Npr1 and Npr3. The Npr1 polymorphism previously described in a Japanese population was not detected among their Italian patients, but they detected another polymorphism in the 3′-untranslated region of Npr1. Interestingly, the same polymorphism was recently described by others [9], and it appears to have a functional impact on the stability and/or translation of the corresponding mRNA transcript in transfected cells. The present study used young normotensive Italian subjects, and showed that the polymorphism, which was described to be associated with decreased concentration of Npr1 mRNA, was more prevalent in individuals with a positive family history of hypertension. Moreover, individuals who carry that variant of the polymorphism show an increase in isovolumic relaxation time (IVRT) [8]. IVRT is an echo-Doppler index of diastolic filling whose variance is determined for a great part by familial factors [10]. Alterations of diastolic function may constitute one of the earliest signs of ventricular dysfunction, before LVH becomes readily apparent. As with most case–control studies, it is expected that the current study will be followed by many others either confirming or failing to replicate this finding. The most common source of errors in such studies is that of unsuspected and uncontrolled stratification within the case and control populations [11]. One way to improve on the design is to use family-based controls, and such approaches will certainly be desirable in future studies. However, even when family-based controls are used, erroneous conclusions can be drawn because of inappropriate sample size. A good example may be that of the T235 allele of the angiotensinogen gene, which was found to be associated with hypertension in some studies, but not in others [12]. However, a careful review of these studies revealed that this association was rejected mostly in those studies that used inappropriate sample sizes, and thus had low statistical power [12]. Of note, the current study [8] used a fairly small sample size for both its case and control populations. Consequently, the proof will lie in the use of appropriately designed replication studies. Nonetheless, the study by Pitzalis et al. [8] is interesting because it announces an era where particular gene variants will be associated with either improved or worsened cardiovascular prospects. It will be particularly interesting to see whether further human studies confirm a cardioprotective role for natriuretic peptide-mediated signalling, as the latter appears to have important cardiovascular effects in animal models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".