Composite Metabolic Indices for Incident Cardiometabolic Multimorbidity: Evidence from the CHARLS and ELSA Cohorts
This study utilizing data from the CHARLS and ELSA cohorts demonstrates that composite metabolic indices, particularly TyG-WHtR and ICMRS, are significantly associated with incident cardiometabolic multimorbidity, with the TyG-WHtR index showing the strongest and most consistent predictive performance across both Chinese and UK populations.
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As people grow older, their bodies often face a quiet accumulation of health challenges. It is rarely just one thing; instead, conditions like high blood pressure, type 2 diabetes, and heart disease tend to arrive together, forming a cluster that doctors call cardiometabolic multimorbidity. This combination is particularly dangerous because it makes the body more fragile, increases the risk of death, and places a heavy burden on families and healthcare systems. For decades, doctors have relied on single measurements, such as a fasting blood sugar test or a cholesterol reading, to gauge a person's risk. However, these isolated numbers often fail to capture the full picture of how a person's metabolism and body shape interact over time. Scientists have long suspected that combining these different signals into a single score might offer a clearer warning system, but it remained unclear which combination worked best or if a tool developed for one group of people would work for another.
To answer these questions, researchers turned to two massive, long-running studies that have been tracking the lives of older adults for years: one in China and one in the United Kingdom. By looking at data from thousands of participants in both countries, the team set out to test five different composite scores. These scores were designed to blend information about how the body handles sugar and fat with measurements of where fat is stored on the body. The researchers wanted to see if these combined tools could predict who would develop multiple heart and metabolic diseases in the future, and whether one specific tool stood out as the most reliable across different cultures and lifestyles.
The study followed 8,682 older adults from China and 2,422 from the UK, all of whom were free of these combined diseases at the start. Over several years of follow-up, the researchers watched to see who developed new cases of cardiometabolic multimorbidity. They calculated five different risk scores for each person at the beginning of the study. One score, known as TyG-WHtR, combined a measure of insulin resistance with the ratio of a person's waist size to their height. Another, called ICMRS, was a broader score that summed up standardized values for insulin resistance, body shape, and a specific type of cholesterol imbalance. The team then compared how well each of these five scores predicted the onset of disease.
The results showed that all five scores were generally linked to a higher risk of developing these combined diseases. However, the strength of the link varied significantly depending on the specific score and the country where the people lived. In the Chinese cohort, every single one of the five scores was a strong predictor. The score that performed best in this group was TyG-WHtR; people in the highest category for this score were more than three times as likely to develop the diseases compared to those in the lowest category. The second-best performer in China was a score called ICMRS. In the UK cohort, the pattern was similar but weaker. While TyG-WHtR and ICMRS remained significant predictors, one of the other scores, AIP-BRI, failed to show a statistically clear link to future disease in the British group.
This difference in strength between the two countries was a major finding. The researchers found that the association between these metabolic scores and future disease was consistently stronger in China than in the UK. For example, the risk increase associated with a higher TyG-WHtR score was much steeper for the Chinese participants. The team also looked at the ability of these scores to distinguish between those who would get sick and those who would not. They found that the ICMRS score had the best overall ability to separate these groups in both countries, while TyG-WHtR was particularly good at identifying risk in the Chinese population. When the researchers tested whether these scores worked differently depending on a person's age, sex, or smoking status, the results were mostly stable, though some variations appeared in the Chinese group related to obesity and smoking habits.
The study suggests that while no single tool is perfect for every situation, combining metabolic data with body shape measurements offers a better view of risk than looking at blood tests alone. The TyG-WHtR score, which is simple to calculate using a tape measure and a standard blood test, emerged as a particularly stable and powerful tool, especially in the Chinese population. The ICMRS score, which requires a slightly more complex calculation but covers more ground, showed the best overall ability to rank risk across both populations. The researchers noted that these tools are not replacements for a full medical exam but could serve as useful, low-cost screening methods to identify people who need closer attention. The findings highlight that while these metabolic signals are universal, the way they predict disease can vary depending on the population, suggesting that risk assessment tools may need to be tuned to the specific characteristics of the people being studied.
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