For weight loss, men and women need different foods for breakfast

New research has found that men’s and women’s metabolisms favor eating different foods at breakfast time and could be key to developing personalized nutrition strategies that help to address health issues or lose weight.

Diet and nutrition are regularly put forward as a way of addressing health problems like increased blood pressure, high blood sugar, excess body fat and high cholesterol levels. Together, this cluster of conditions, often referred to as ‘metabolic syndrome,’ increase the risk of heart disease, stroke, and type 2 diabetes.

Plenty of research has compared the pros (and cons) of different diets, from keto and paleo to intermittent fasting, and their effect on weight loss. But what influence does sex have on diet and dieting? A new study by researchers from the University of Waterloo, Canada, has used mathematical models of metabolism to answer that question as it relates to the ‘most important meal of the day,’ breakfast.

“Lifestyle is a big factor in our overall health,” said Stéphanie Abo, a PhD candidate in applied mathematics and the study’s corresponding author. “We live busy lives, so it’s important to understand how seemingly inconsequential decisions, such as what to have for breakfast, can affect our health and energy levels. Whether attempting to lose weight, maintain weight, or just keep up your energy, understanding your diet’s impact on your metabolism is important.”

There are six basic nutrients essential for proper body functioning and maintaining overall health: carbohydrates, proteins, lipids (fats), vitamins, minerals, and water. The researchers used a sex-specific, whole-body mathematical model of metabolism that simulated the dynamics of key metabolites after various mixed meals. They aimed to quantify sex differences in carbohydrate and lipid metabolism at the whole-body level and propose mechanisms that drove these differences.

Mathematical modeling is a research method that describes, predicts, and simplifies complex real-world systems and occurrences using math formulas. A model that’s based on underlying human biology and biochemistry can reveal the causal chain of events that connect variation in one quantity to variation in another. As such, it’s useful for investigating a large number of metabolism-related questions.

“By building mathematical models based on the data we do have, we can test lots of hypotheses quickly and tweak experiments in ways that would be impractical with human subjects,” said Anita Layton, a Waterloo professor of applied mathematics who co-led the study with Abo.

The researchers calibrated their model using data from experiments involving both high- and low-carbohydrate and high- and low-fat meals to ensure that the parameters of the model were robust and reflected real physiological processes. They found that sex-related metabolic differences were more pronounced after short-term fasting.

After fasting for several hours, men’s metabolisms responded better, on average, to a meal that was high in carbs – so, a breakfast of something like oats or grains. Women, on the other hand, were better off eating a brekkie with a higher percentage of fat, like an omelet or an avocado. Their findings suggest that differences in how the liver and adipose tissue store nutrients drive these differences in metabolism between the sexes.

“Since women have more body fat on average than men, you would think that they would burn less fat for energy, but they don’t,” said Layton. “The results of the model suggest that women store more fat immediately after a meal but also burn more fat during a fast.”

The researchers say their model’s ability to accurately predict real-world metabolic responses to mixed meals has important implications: informing personalized nutrition strategies, advancing research into metabolic diseases, and supporting drug development and testing by simulating metabolic responses to new drugs. Moving forward, they hope to develop more complex versions of their metabolism models that factor in an individual’s weight, age, or stage in the menstrual cycle.

The study was published in the journal Computers in Biology and Medicine.

Source: University of Waterloo