Association between birth weight and mortality in adulthood in a cohort from North-West Italy

Lorenzo Milani1, Elena Farina2, Paola Armaroli3

  • 1Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Orbassano, Turin, Italy.

Public Health
|August 30, 2025
PubMed

Insights

Low birth weight increases the risk of adult mortality from all causes and cardiovascular diseases. This risk is sex-specific, impacting cardiovascular mortality in females and nervous system diseases in males.

Area of Science:

  • Public Health
  • Epidemiology
  • Longitudinal Studies

Background:

  • Globally, approximately 15% of newborns are underweight, posing potential long-term health risks.
  • Low birth weight is linked to increased mortality from various causes, including cardiovascular disease.
  • High birth weight is also associated with increased risk for certain conditions like cancer.

Purpose of the Study:

  • To investigate the association between birth weight and adult mortality from diverse causes.
  • Utilize data from the Turin Longitudinal Study (TLS) for comprehensive analysis.

Main Methods:

  • Analysis of a health-administrative cohort from the Turin Longitudinal Study (TLS).
  • Inclusion of 2992 individuals born in the 1920s, followed from 1971 to 2013.
  • Assessment of birth weights (<2500g, <2800g, ≥4000g) and mortality from all causes, cardiovascular, cancer, respiratory, nervous system, and digestive diseases using survival and competing risk models.

Main Results:

  • Low birth weight (<2500g or <2800g) emerged as a significant risk factor for all-cause mortality.
  • Low birth weight was associated with increased risk of death from cardiovascular diseases.
  • Sex-stratified analysis revealed low birth weight linked to cardiovascular mortality in females and nervous system disease mortality in males.

Conclusions:

  • Low birth weight is a critical risk factor for adult mortality, encompassing all causes and specific conditions like cardiovascular and nervous system diseases.
  • Findings underscore the importance of addressing non-normal birth weights to mitigate long-term health outcomes.
  • The study highlights the need for public health policies aimed at reducing the incidence of low birth weight.
Abstract

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
671
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
231
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
196
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
600
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
340