Cardiometabolic Risk Patterns Among Adults: A Latent Class Analysis from the Hoveyzeh Cohort Study
This prospective cohort study of 1,660 adults from the Hoveyzeh Cohort Study identified two distinct cardiometabolic risk profiles using latent class analysis but found that membership in the high-risk profile was not significantly associated with an increased risk of developing incident dyslipidemia over a five-year follow-up.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
The human body is a complex machine where different systems talk to each other constantly. When it comes to heart health, doctors have long known that certain warning signs often appear together. High blood pressure, excess weight around the middle, and trouble with blood sugar are not just random problems; they frequently cluster in the same people, creating a dangerous mix that raises the risk of heart disease and stroke. For decades, the standard approach to understanding this risk has been to look at each factor separately, as if checking the oil, the tires, and the brakes on a car one by one. However, scientists have begun to wonder if looking at these factors as a group might reveal hidden patterns. Perhaps people fall into distinct groups, or profiles, where specific combinations of lifestyle habits and body conditions create unique risk signatures that simple checklists miss. Understanding these natural groupings could help researchers see who is truly at risk and why, moving beyond a one-size-fits-all view of health.
In a large study of adults in southern Iran, researchers set out to find these hidden groups. They looked at data from 1,660 men and women who were healthy enough to have normal blood fats at the start of the study. The team examined five specific things for each person: whether they had high blood pressure, whether they had diabetes, how active they were, how much saturated fat they ate, and whether they carried excess weight around their waist. Using a statistical method that acts like a sorting machine to find natural clusters, the researchers asked if these adults could be divided into distinct groups based on how these five factors appeared together. They found that the group did indeed split into two clear profiles. The first group, which made up about 72 percent of the people, generally had low chances of having these risk factors. The second group, comprising the remaining 28 percent, was very different. People in this second group were much more likely to have high blood pressure, diabetes, and a large waist, and they were also more likely to be less active and eat more saturated fat. This second group represented a clear, high-risk profile where multiple problems crowded together.
The researchers then watched what happened over the next five years. They wanted to see if belonging to that high-risk group meant a person was more likely to develop high blood fats, a condition known as dyslipidemia, which is a major cause of heart trouble. During this time, more than half of the participants developed this condition. Surprisingly, the study found that belonging to the high-risk group did not predict who would get sick. The people in the high-risk group were just as likely to develop high blood fats as the people in the lower-risk group. The statistical analysis showed no real difference between the two groups regarding this specific outcome. This result was unexpected because the second group had so many more warning signs. It suggests that while these risk factors do cluster together in distinct patterns, that specific pattern does not necessarily tell us who will develop high blood fats in the future.
The study also revealed who was more likely to fall into the high-risk group. The second group contained a higher proportion of women and older adults, particularly those over 55. It also included more people who had less formal education and more people living in cities compared to the countryside. Despite these clear differences in who made up the groups, the groups themselves did not act as a crystal ball for predicting the specific disease the researchers were tracking. The authors note that this does not mean the high-risk group is healthy; they still carry a heavy burden of other metabolic problems. It simply means that the specific combination of factors they shared did not make them more likely to develop high blood fats than the others. The findings suggest that while we can identify people with clustered risks, those clusters might not be the best way to predict this particular health outcome. Instead, the study reinforces the idea that heart health risks are diverse and that looking at people as individuals with unique combinations of habits and biology is essential, even if those combinations do not always point to the same future illness.
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