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Could your meal timing weaken your bones? A recent study suggests that habits like skipping breakfast or eating late-night dinners may heighten the risk of developing osteoporosis. This pioneering research is the first to highlight a clear link between such eating patterns and an increased chance of bone fractures due to osteoporosis.
Osteoporosis affects 10 million people in the U.S. and is four times more prevalent in women. It develops when bone mineral density declines, leading to weaker bones that fracture more easily. Although smoking, excessive alcohol use, and a sedentary lifestyle are known risk factors for osteoporosis, the influence of overall dietary habits has been less understood, prompting researchers to examine the role of daily eating habits.
Osteoporosis is known as a “silent” disease because it often presents no symptoms, and individuals may be unaware of the condition until a bone fracture occurs. It is the primary cause of fractures in postmenopausal women and older men, most commonly affecting the hip, spine vertebrae, and wrist.
To explore how diet and habits affect osteoporosis risk, researchers examined data from over 900,000 Japanese adults aged 20 and older in a medical database. Participants completed a questionnaire about their habits during a health checkup and were monitored for an average of over two years to track the occurrence of osteoporotic fractures.
The researchers discovered that individuals who stated they skipped breakfast more than three times per week had an 18% higher likelihood of developing fractures. Those who indicated they ate dinner less than two hours before bedtime more than three times a week were 8% more likely to experience fractures.
These findings indicate that preventing osteoporosis and fractures involves not only maintaining consistent, healthy eating habits but also adopting broader lifestyle improvements, such as regular exercise and quality sleep.
To view the original scientific study click below:
Dietary Habits and Osteoporotic Fracture Risk: Retrospective Cohort Study Using Large-Scale Claims Data
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