Being obese in old age will not help you survive cardiovascular disease, contrary to widely-held beliefs, a new study reveals
In recent years, research has popularized the notion of an ‘obesity paradox,’ which suggests that being overweight can help to insulate older people with chronic diseases, helping them to outlive their trimmer counterparts.
But the new study took aim at debunking that notion, and showed that the paradox was based on a number of biases.
It is well documented that obesity increases the risk of developing heart diseases – among many others – and the new research demonstrates that the condition does not improve the resilience of older people against these diseases either.
A new study found that obese and overweight people are just as at risk of dying from heart diseases as others, refuting the so-called ‘obesity paradox’
The American obesity epidemic is so threatening because obesity increases the risks of innumerable often fatal diseases, including 40 percent of cancers, diabetes, and stroke, as well as heart diseases.
Yet, the obesity paradox has created a – perhaps misleading – impression that while obese people may be more likely to get these diseases they are also more likely to survive them.
Previous research has particularly looked at the chances for survival of events like heart attacks and major surgery, as well as more long-standing conditions like heart failure and high blood pressure.
The clearest finding from those studies was that people who were underweight had the worst survival rates.
Both the new study from New York University (NYU) and previous work, recognize that diseases and general declining health can lead to ‘wasting’ or the deterioration of muscle and fat tissue as people get sicker.
The underlying assumption, then, is that overweight and obese people have more tissue to lose, and, therefore, more time to live. The paradox is akin to starvation survival principles, that suggests that more fat means more calories to burn for energy, and more energy means better survival rates.
‘Given that many diseases result in wasting at the end of life, the notion that extra catabolic reserve can prolong survival makes intuitive sense,’ said Dr Chang.
But according to the new study, the theory doesn’t really hold water.
Previous studies have looked at the weights and conditions of people with existing cardiovascular diseases, categorizing subjects as obese or not based only on their reporting of their weights and body mass indexes after diagnosis.
Because disease can lead to both wasting and death, the NYU researchers figured that the these categories might be misleading.
Obesity is also known to force the heart to work harder, meaning that overweight and obese people who have very serious conditions are more likely to die shortly after diagnosis, missing the window for study, while more ‘robust’ obese people are more likely to live long enough to be signed up for research.
So Chang and her team looked at data collected since 1992 on more than 30,000 people over 50.
To account for ‘wasting’ and a selection bias, they compared pre-diagnosis weights to survival outcomes for recently diagnosed people, and current weights to survival outcomes for people who had been living with chronic heart diseases for some time.
Just as in previous research, the NYU study found that obese people who had long had pre-existing heart conditions had between 18 and 36 percent lower risks of death than people of other weights.
But, when the compared survival rates for the same conditions using pre-diagnosis weights, the obesity paradox disappeared, and being overweight provided no survival advantage.
In other words, those who were obese and then got diagnosed with a chronic heart disease were no more likely to survive than others. Those whose current weights were ‘normal’ after their diagnosis may well have been obese to begin with and simply were declining.
Removing the long-term disease and current weight comparison essentially leveled the playing field, according to the authors.
‘The loss of an obesity paradox when switching from prevalent to incident cases and pre-diagnosis weight in the same dataset suggests that prevalent models are likely biased by factors such as disease-related weight loss and selective survival,’ Dr Chang said.