Why some people with very risky lifestyle and lots of risk factors can live longer than others free of risks? A long-standing question in medical practice.


This is an excerpt of an article written by me submitted recently to be assessed for publication in a scientific journal that I would like to share with you.

In medical practice is common to find situations that might call into question any practitioner on the epidemiological component of cardiovascular disease (CVD).

How many times we assess patients with multiple cardiovascular risk factors (CRF) and poor adherence to the treatment and they never develop one expected disease resulting from these CRF. While at the other extreme, there are individuals without CRF and with an appropriate lifestyle who begin suffering from an “unexpected” acute or chronic heart disease.

Taking into account the above mentioned, this article attempts to answer the following question: which would be the wiser perspective to address the issue of cardiovascular risk in relation to causation in CVD?

First, it is worth noticing that since Hippocrates, father of medicine, who advised to seek actively in the external causes of disease to the casualty criteria issued in 1965 by the eminent emeritus professor of medical statistics at the University of London, Sir Austin Bradford Hill (temporary , strength of association, biological gradient, specificity of association, consistency of association, biological plausibility, consistency with current knowledge, analogy and experimental demonstration), the issue of relationship between cause and effect has resulted to be very controversial in the epidemiology over the years. (1)

To answer the initial question, the author considers necessary to highlight that epidemiology defines the concept of an illness as an event, circumstance, feature or combination of these factors playing an important role in causing the disease. Obviously, the cause must precede the disease. It is said that a cause is “enough” when it inevitably occurs or starts the disease and “necessary” when the disease cannot develop in its absence. A sufficient cause is usually not a single factor, but often is a set of several components. In general, it is necessary to identify all components of a sufficient cause to carry out effective prevention, since the elimination of one of these components can interfere with the action of others and thus prevent the disease (2)

For example, smoking is one of the components of the sufficient cause of acute myocardial infarction (AMI) but it is not enough by itself to cause disease: some people smoke for 50 years without developing coronary heart disease. Thus, there are other necessary factors. However, quitting smoking reduces the incidence of IMA, but other causal components do not change.

In the same line, one should be aware that there are causality or causation factors as predisposing factors such as age, sex or preexisting health condition that might create a state of sensitivity for an agent prone to cause the disease. In addition, there are facilitating factors such low income, poor nutrition, inadequate housing and insufficient health care, which may favor the development of disease. These factors may be necessary but rarely sufficient to cause disease.
In order to refer to factors positively associated with risk of developing a disease but not enough to cause its expression, the term risk factor is used (3)
The above clearly reflects the complexity of the genesis of any disease, constituting a paradigm in this sense, CVD, for which have been already identified over 177 risk factors, although, in near future, this figure is expected to rise (4)

In the first stage of evolutionary development of epidemiology as a science, the model of
causality was a model of single causes to single effects that was soon replaced by the multiple causes-unique effects.  Later, with the so-called Second Revolution in Epidemiology began studies on risk factors that laid the foundation for the emergence of a new causal paradigm in epidemiology: the multiple causes- multiple effects. A paradigm that is unsurpassed to this day being a step forward in the development of epidemiology, although it is a part of the linear approach that has dominated this science to date. Recently, a new concept has come up, the causality complex concept, which does not deny the existence of linear causality but enables the study of a complex system since the perspective of complexity and from simplicity (5)

The focus of causality based on complexity proposes a different analysis model, where the causes are not unique or multiple, but complex. Theoretical models for addressing causation under the assumption of complexity are not fully developed, we would say are being built right now, so there is not extensive documentation regarding its practical application (6)

Nowadays, the cardiovascular prediction models are trending worldwide (7) The author of this paper has published previously some approaches on this issue focusing in arterial hypertension since early stages of life to predict the disease in adulthood using conventional methods (8)

which might lack of the necessary reliability due to the classical statistical methods used.

I firmly believe that the medical community must decipher better tools to handle the with the complex casualty problem to first understand the contemporary health-disease model and secondly to find new ways to predict since the standpoint of complexity.

In the author’s opinion, the effective application of the theory of complex causality, would be the most effective way to address the issue of cardiovascular risk in relation to causation in CVD and to do it since the perspective of predisease (before the disease be overt) would be key in the long term (9,10)


  • Bradford Hill A. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295-300.
  • Porta M, ed. A Dictionary of Epidemiology. 5th. edition. New York: Oxford University Press; 2008. p. 34-37, 65-66, 82-84, 100-103, 116, 129-130, 152-154, 237-238.
  • Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nat Rev Genet. 2007;8:253-62.
  • Ordovas JM. Interacciones entre genes y entorno y factores de riesgo cardiovascular. Rev Esp Cardiol Supl. 2009;9:39B-51B.
  • Kundi Michael. Causality and the interpretation of epidemiologic evidence. Ciênc. saúde coletiva [serial on the Internet]. 2007  Apr [cited  2011  July  04] ;  12(2): 419-428.

Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-81232007000200018&lng=en.  doi: 10.1590/S1413-81232007000200018.

  • Phillips CV, Goodman KJ: Causal Criteria and Counterfactuals; Nothing More (or Less) than Scientific Common Sense. Emerg Themes Epidemiol.2006,3:5.
  • Al-Rawahi A, Lee P. Applicability of the Existing CVD Risk Assessment Tools to Type II Diabetics in Oman: A Review. Oman Med J. 2015 Sep;30(5):315-9. doi: 10.5001/omj.2015.65
  • Perez Fernandez GA, Grau Abalo R. From the prehypertensive adolescent to the hypertensive adult. Is possible to predict the conversion? Arch Cardiol Mex. 2012;82(2):112–9.
  • Perez Fernandez GA. The arbitrariness of the cut off points. a reflection since the perspective of predisease. Arch Cardiol Mex. 2012;82(3).
  • Perez Fernandez GA. Prediction of cardiovascular disease from the early stages of life: A forgotten issue? Qatar Medical Journal 2016; 1(6). DOI: 10.5339/qmj.2016.6. (available at: http://www.qscience.com/doi/full/10.5339/qmj.2016.6)

Note. This editorial has been written by Dr. Guillermo Alberto Perez Fernandez, author of this blog, and reflects his personal opinion about the topic.

Please rate this post

3 thoughts on “Why some people with very risky lifestyle and lots of risk factors can live longer than others free of risks? A long-standing question in medical practice.”

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s