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GNRI, CONUT, and PNI indices predict metabolic-associated fatty liver disease in Chinese adultsCould your nutrition score predict fatty liver disease risk before symptoms appear?

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Key Takeaway
Note that GNRI and PNI indices show strong associations with MAFLD risk in Chinese adults.

This retrospective cross-sectional study analyzed data from 10,901 cases at the First Affiliated Hospital of the Army Medical University in China. The population included 2,426 cases with MAFLD and 8,475 cases without MAFLD. Researchers evaluated the GNRI, CONUT, and PNI nutritional indices to determine their association with the occurrence of MAFLD.

The analysis revealed that individuals in the fourth quartile of GNRI had a 7.01 times higher risk of MAFLD compared to those in the first quartile (OR = 7.01; 95% CI: 5.08–9.67). Similarly, the fourth quartile of PNI was associated with an 1.82 times higher risk (OR = 1.82; 95% CI: 1.51–2.19). For predictive performance, GNRI and PNI achieved an AUC of 0.909, whereas the specific AUC for CONUT was not explicitly stated in the results summary.

The study noted a stronger association between nutritional indicators and MAFLD in individuals under 35 years old without hyperlipidemia or hyperglycemia. Superior performance of GNRI over CONUT and PNI was observed in women, individuals with BMI < 24, and those under 35 years of age. No safety data, adverse events, or discontinuations were reported as this was a cross-sectional analysis of existing examination data.

Key limitations include the cross-sectional design, which precludes causal inference regarding the development of MAFLD. The study setting was limited to a single hospital in China. These findings support the use of GNRI and PNI for clinical screening and intervention of MAFLD but require validation in prospective cohorts.

Imagine walking into a doctor's office and having a simple blood test that could flag a hidden liver problem. That is the hope behind this new look at nutrition scores. Researchers examined over 10,000 Chinese adults to see if three specific nutritional markers could predict metabolic-associated fatty liver disease, or MAFLD. This condition involves fat building up in the liver, often without pain or obvious warning signs.

The study compared people with the disease to those without it. Those with the lowest nutritional scores faced a massive jump in risk. Specifically, people with the worst scores on one key marker had seven times the risk of having fatty liver disease compared to those with the best scores. Another marker showed an 1.8 times higher risk for those with low scores.

The tests worked best for younger adults, women, and people with a healthy body weight. Interestingly, the link between these nutrition scores and liver disease was even stronger in young adults who did not have high blood sugar or high cholesterol. Yet, because the study only looked at data collected at one time, we cannot say for sure that low nutrition scores caused the liver disease.

This research offers a potential new way for doctors to screen for MAFLD early. It suggests that looking at how well a person is nourished might help catch the problem sooner. But remember, this is a connection found in past records, not a guarantee that changing your diet will fix the disease or prevent it in every case.

What this means for you:
Low nutrition scores linked to fatty liver disease, but this study shows connection, not cause.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
BackgroundMetabolic-associated fatty liver disease (MAFLD) has become a major global health issue. Although nutrition is known to be associated with MAFLD, indices such as the Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status (CONUT), and Prognostic Nutritional Index (PNI) have been used for prognostic assessment in diseases like cancer and heart disease, but their relevance and predictive performance in MAFLD, especially across different subgroups in China, still need validation.MethodsThis study is a retrospective cross-sectional study conducted at the First Affiliated Hospital of the Army Medical University in China. A total of 10,901 cases were included (MAFLD group: 2,426 cases; non-MAFLD group: 8,475 cases). Logistic regression analysis was performed using SPSS 27.0 and R 4.5.1 to determine relevant associations (calculating OR values and 95% confidence intervals). Receiver operating characteristic (ROC) curves were plotted for the three nutritional indices—GNRI, CONUT, and PNI—to predict the occurrence of MAFLD in different sex, BMI, and age groups, and area under the curve (AUC), sensitivity, and specificity were calculated. Stratified analysis was performed to study the interaction between MAFLD and different subgroups.ResultsThere is a dose–response relationship between GNRI, CONUT, and PNI and MAFLD. In multivariable models, the risk of developing MAFLD in the fourth quartile of GNRI and PNI was 7.01 times (OR = 7.01, 95% CI: 5.08–9.67) and 1.82 times (OR = 1.82, 95% CI: 1.51–2.19) that of the first quartile, respectively Overall, the predictive performance of GNRI was superior to that of CONUT (AUC = 0.909) and PNI (AUC = 0.909), especially in women, individuals with BMI less than 24, and those under 35 years of age. Stratified analysis showed that in individuals under 35 years old without hyperlipidemia and without hyperglycemia, the association between these three nutritional indicators and MAFLD was stronger.ConclusionGNRI, CONUT, and PNI can all serve as effective indicators for assessing the risk of MAFLD, among which GNRI has the highest application value, especially suitable for female, young people, and individuals with normal weight, and can provide new insights for the clinical screening and intervention of MAFLD.
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