Licia Iacoviello

Licia Iacoviello MD, PhD, full Professor of Hygiene and Public Health at the LUM University, Casamassima (BA), directs the Department of Epidemiology and Prevention of IRCCS Neuromed, Pozzilli (IS) and its Center for Big data and Personalised Health, Neurobiotech, Caserta.

She has been working on the impact of genes and environment and their interaction on cardiovascular risk and on the role of dietary habits in the development of metabolic disorder and chronic degenerative diseases.

Since 2005 she is coordinating the Moli-sani Study, a large population-based cohort study involving over 24,000 adult people in Southern-Italy, in order to assess risk factors related to lifestyle (notably dietary habits) and genetics for chronic-degenerative and metabolic diseases and from 2018, the PLATONE Study a clinical research network for Big-data, machine Learning and personalized health.

In the last 20 years her activity has been mainly devoted to provide scientific bases to the healthy effect of the Mediterranean diet and to understand the determinants of Mediterranean diet adhesion.

Licia Iacoviello has published over 560 scientific articles in international “peer reviewed” journals. She has been listed by in the Top Italian Scientists and in the World’s top 2% scientists.

Mediterranean diet, wine, lifestyles and biological aging.

Licia Iacoviello

Research Unit of Epidemiology and Prevention. IRCCS Neuromed. Pozzilli. Italy

Department of Medicine and Surgery. LUM University. Casamassima. Italy

Biological age (BA) is the hypothetical underlying age of an organism and has been proposed as a more powerful predictor of health than chronological age (CA). The difference between BA and CA (Δage = Ba – CA) reflects the rate of biological aging, with lower values indicating slowed-down aging (i.e., BA << CA). Δage significantly predicted the incident risk of all-cause mortality and first hospitalizations, better then CA.

To estimate BA in the Moli-sani cohort, we adopted a blood-based approach leveraging 36 biomarkers. This method is both easy to implement and replicable in other cohorts, and it outperforms existing clocks in predicting CA. We then employed a deep neural network—an algorithmic model inspired by the structure of the human brain—to analyze these data and build our predictive model.  The biomarkers include the most common pathway associated with chronic disease onset and progression (e.g. liver or renal function markers, markers of lipid and glucose metabolism, inflammation markers and others).

This blood-based BA has been associated with Mediterranean diet. Indeed, 1 standard deviation increase in the Mediterranean dietary score (MDS) was inversely associated with Δage and similar findings were observed with the DASH diet. High dietary polyphenol content explained 29.8%  and 65.8%  of these associations. On the contrary, a diet rich in ultraprocessed foods was associated with an acceleration of biological aging.

Among Mediterranean diet components, Δage was significantly associated alcohol consumption (mainly as wine), with a protective effect of moderate alcohol drinking (current drinkers who drank 1–12 g/day, in comparison with abstainers). The analysis of other modifiable risk factors revealed statistically significant associations of Δage with smoking and obesity, both associated with an acceleration of the aging process.

Lifestyles together with socioeconomic factors explained around 48% of the variance in Δage, underscoring the importance of modifiable factors control, in slowing the aging process.