Smoking accelerates biological aging, which can be seen in the skin, among other things.
Now a group of American and Chinese researchers have found that the aging process accelerates even more when people are lonely or unhappy.
They examined a range of physical and mental factors to discover how they affect biological aging. They discovered that loneliness, poor sleep and feeling unhappy have a more significant negative impact on the aging process than smoking.
‘Biological clock’ used for calculations
“The body and the soul are connected – that’s our main message,” said Fedor Galkin, one of the researchers.
In the journal Aging-US, the scientists explain how they built a digital aging model — a sort of “biological clock” — based on data from 4,846 adult Chinese collected in 2015 for the China Health and Retirement Longitudinal Study (CHARLS). ). They analyzed 16 biomarkers in the blood, including cholesterol and glucose. They also looked at blood pressure, BMI and lung function.
Illness ages the body
Using that ‘clock’, the researchers calculated the aging rate in 2,617 people with conditions such as stroke, liver disease and lung disease. These diseases are known to speed up the aging process.
For example, they found that aging was more advanced in the sick people than in 4,451 healthy adults of the same chronological age, gender and geographic location.
Loneliness has the most influence
The average effect of the different diseases was 18 extra months on top of the people’s actual age.
However, they found the biggest surprise when they looked at psychological factors. They found that people who were unhappy or lonely were on average 19.8 months older than their real age. In comparison, smokers were calculated to be 15 months older than non-smokers of the same age.
The scientists emphasize that this only concerns model calculations based on their ‘biological clock’.
They have not yet investigated whether people with psychological problems actually age faster. In the future, this important research can be performed by testing the model calculations using repeated experiments over a longer period of time.