Manzato, Matteo

Abstract Aims Social isolation (SI) is associated with a higher risk of cardiovascular disease (CVD). One mechanism linking SI and CVD is accelerated biological ageing, which can be assessed using artificial intelligence-enabled electrocardiography (AI-ECG). We investigated whether longitudinal changes in SI status are associated with changes in biological ageing patterns. Methods and results A retrospective longitudinal cohort study including 17 663 individuals without significant CVD who completed ≥2 Berkman and Syme Social Network Index and had paired ECGs within 1 year of each questionnaire. Longitudinal SI status was defined by comparing baseline and follow-up SI status. Ageing trends were assessed using Δage-gap, defined as the change in the gap between AI-ECG-predicted age and chronological age from baseline to follow-up. Linear regression models predicting Δage-gap and survival analyses with Kaplan–Meier and multivariable Cox regression for all-cause mortality were performed. Persistently connected individuals were older [63.1 (52.1, 70.6) years] and had a higher prevalence of hyperlipidaemia and hypertension, yet demonstrated more favourable ageing trajectories. Similarly, those who became connected showed a non-significant attenuation of accelerated ageing. In multivariable linear regression, being persistently isolated [β = +0.412 years, P < 0.001] or becoming isolated [β = +0.438 years, P = 0.012] was associated with accelerated ageing compared to being persistently connected. In survival analyses, becoming isolated (aHR = 1.94, 95% CI = 1.53–2.46, P < 0.001) and being persistently isolated (aHR = 1.66, 95% CI = 1.40–1.96, P < 0.001) were associated with significantly higher all-cause mortality, independent of chronological age and comorbidities. Conclusion Persistent social connection is associated with slower biological ageing and lower mortality. The effects of SI on ageing and survival appear to be potentially reversible, calling for a prospective study to evaluate interventions aimed at improving both the social and biological burden of SI.

Background Coronary flow reserve reflects microvascular function, whereas filling pressure indicates myocardial hemodynamic burden. In angina with nonobstructive coronary arteries, abnormalities in either could contribute to a supply–demand mismatch; however, their combined prognostic significance remains unclear. Methods Patients with angina with nonobstructive coronary arteries who underwent invasive coronary reactivity tests were studied retrospectively. Coronary microvascular dysfunction (CMD) was coronary flow reserve <2.5. An artificial intelligence–enabled ECG marker of elevated filling pressure (AIEFP) was applied as a scalable prognostic marker of myocardial hemodynamic stress. Associations with major adverse cardiovascular events were evaluated using Cox regression and propensity score overlap weighting. Results Among 1957 patients with angina with nonobstructive coronary arteries (median age, 51.8 years; 67.2% women), CMD+ was identified in 26.4% and AIEFP+ in 6.6%. Over 8 years, 204 patients (10.4%) experienced major adverse cardiovascular events. In nested models, both CMD (hazard ratio [HR]=1.42 [95% CI=1.13–1.78]; P =0.002) and AIEFP (HR=2.28 [95% CI=1.67–3.11]; P <0.001) remained associated with major adverse cardiovascular events after adjustment. Adding AIEFP to the base model improved model fitness (Δχ 2 =23.65, P <0.001; ΔAkaike Information Criterion=−21.7) and modestly improved discrimination (ΔHarrell concordance index=0.02, P =0.02). In a 4‐group model, adjusted risks increased in a graded manner. Overlap‐weighted analysis after covariate balance demonstrated distinct temporal risk trajectories: CMD+/AIEFP+ had the highest 1‐year risk (12.7%), and AIEFP+ had the highest cumulative 8‐year risk (57% in CMD− and 51.8% in CMD+). Conclusions CMD and AIEFP provide complementary prognostic information, with AIEFP remaining significantly associated with outcomes after multivariable adjustment. A dual‐axis framework integrating myocardial hemodynamic stress (AIEFP) and microvascular function (coronary flow reserve) may enhance risk stratification and enable identification of clinically distinct angina with nonobstructive coronary arteries phenotypes with divergent trajectories.

Background Inadequate preventive dental care may contribute to inflammatory conditions such as periodontitis, increasing cardiovascular disease risk. Artificial intelligence–enabled electrocardiography (AI‐ECG) algorithms accurately estimate cardiovascular disease risk. We investigated the association between inadequate preventive dental care and the AI‐ECG–estimated risk of atrial fibrillation, aortic stenosis, low ejection fraction, cardiac amyloid, cardiovascular aging, and all‐cause mortality. Methods We retrospectively analyzed 266 637 Mayo Clinic patients (2018–2024) who completed a Social Determinants of Health Survey and had an ECG performed within 1 year of survey completion. AI‐ECG algorithms generated continuous risk scores (0 or 1) for atrial fibrillation, aortic stenosis, low ejection fraction, and amyloid. Cardiac aging was assessed by delta age, calculated as AI‐ECG–estimated physiological age minus the chronological age. Associations with preventive dental care were analyzed using logistic regression. Cox regression evaluated dental care predictiveness on all‐cause mortality. Results Among 266 637 individuals (mean age, 59.5±16.4 years; 51.1% women), 49 918 (18.7%) reported inadequate preventive dental care. These individuals had higher delta age and increased AI‐ECG–predicted risks of atrial fibrillation, aortic stenosis, amyloid, and reduced ejection fraction (all P <0.001). In multivariable logistic regression models including all AI‐ECG scores, inadequate preventive dental care was associated with higher probabilities of low ejection fraction (odds ratio [OR], 1.80 [95% CI, 1.69–1.91]), atrial fibrillation (OR, 1.10 [95% CI, 1.04–1.17]), aortic stenosis (OR, 1.90 [95% CI, 1.64–2.20]), amyloid (OR, 1.18 [95% CI, 1.12–1.25]), and higher delta age (OR, 1.03 [95% CI, 1.02–1.03]) (all P <0.001). Inadequate preventive dental care predicted all‐cause mortality after adjusting for demographics, risk factors, and AI‐ECG algorithms (HR, 1.45 [95% CI, 1.38–1.53], P <0.001). Conclusions Inadequate preventive dental care is associated with subclinical cardiovascular disease. AI‐ECG algorithms may facilitate cardiovascular risk stratification among individuals with limited preventive dental care access.

Background α‐Klotho is a multifunctional protein implicated in vascular aging. Declining Klotho levels are associated with arterial stiffness, endothelial dysfunction, and vascular calcification. We investigated the associations between circulating klotho, aortoiliac calcification, endothelial progenitor cells, and 10‐year survival. Methods In this retrospective cohort, blinded clinicians scored calcification on computed tomography (0–10 scale: aorta 6 points, iliac 4 points). Plasma klotho was measured by ELISA; endothelial progenitor cell subsets were quantified via flow cytometry. Mortality data were obtained from the Minnesota Death Registry. Results This study included 148 participants (mean age 72.4±9.6 years; 41% female). Median calcification score was 3 (interquartile range, 1–5). Patients with low calcification had higher Klotho levels (669 [567–841] versus 556 [457–678] pg/mL; P <0.01), with Klotho inversely correlated with calcification (r=−0.315, P <0.01). Stratified by calcification location and severity, the highest Klotho levels were seen in patients without aortic or iliac calcification ( P =0.015). Over 10 years, low Klotho was independently associated with higher mortality (hazard ratio [HR], 3.056 [95% CI 1.288–7.878]; P =0.014). Klotho inversely correlated with mature osteocalcin‐positive EPCs; B=−0.16 (95% CI, −0.29 to −0.02; P =0.027). Immunohistochemistry and immunofluorescence confirmed endothelial Klotho expression in internal mammary artery. Conclusions Lower circulating Klotho levels are associated with increased aortoiliac calcification and higher 10‐year mortality, supporting a role for Klotho in vascular aging.

Lipoprotein a (Lp(a)) is a lipid biomarker that binds cholesterol and bears independent cardiovascular risk. Strategies to lower the level of Lp(a) and mitigate such risk are important both for primary and secondary prevention. Currently there are no approved therapies targeting Lp(a) directly. Lipid lowering therapies prescribed routinely may have no effect on Lp(a) levels. Some agents such as niacin and estrogens can significantly decrease Lp(a), but their use is not recommended due to their adverse safety profile. Statins increase Lp(a) levels by 10–20%, questioning the benefit of such therapy when this biomarker is elevated. The Food and Drug Administration (FDA) endorses new agents to address dyslipidemia such as proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9-i) and Inclisiran, a small interfering RNA. These approaches have been shown to also significantly reduce Lp(a), but more clinical data is needed before implementing their use in clinical practice. Clinical trials are currently ongoing to test the efficacy of newly developed antisense oligonucleotides and small interfering RNAs targeting the gene encoding for Lp(a) in hepatocytes, while other investigations assess small molecules that inhibit Lp(a) assembly. This review summarizes the pathophysiology and clinical implications of Lp(a) elevation, and focuses on proposed Lp(a) therapies and the current state of the clinical trials of such novel agents.