Evidence for a protein leverage effect on food intake in a Norwegian population

DOI: https://doi.org/10.1016/j.appet.2026.108581

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Abstract

The global obesity epidemic is often attributed to multifactorial causes, yet the protein leverage hypothesis offers a specific mechanism involving the dilution of dietary protein in modern food environments. This hypothesis suggests that humans prioritize a specific protein target, leading to excess energy consumption when the protein proportion of the diet decreases. While several randomized controlled trials and pediatric studies have supported this, few large scale observational studies have examined these effects in adult European populations. This pre-registered study aimed to explore the relationship between habitual dietary protein, total energy intake (TEI), and BMI within a general population. The researchers utilized cross-sectional data from the seventh survey of the Tromsø Study (2015-2016), focusing on a cohort of 11,152 Norwegian adults aged 40 to 99 years.

The analysis provided strong support for a partial protein leverage effect on energy intake. Total energy intake was negatively associated with the proportion of dietary protein (L = -0.36, p < .001) and positively associated with dietary fat (L = 0.33, p < .001). While the researchers intended to test the link between protein and BMI, the data showed a weak negative relationship between total energy intake and BMI (p < .001), rendering the sample unsuitable for testing protein's effect on BMI. Ultra-processed food (UPF) consumption was positively associated with total energy intake (b = 554, p < .001) and negatively associated with the proportion of dietary protein (b = -2.0, p < .001). Additionally, a subsample analysis (n = 880) revealed that plasma FGF21 levels were negatively associated with percent protein in the diet (b = -0.05, p = .001).

Study Design and Methodology

This cross-sectional study analyzed data from the Tromsø7 survey (2015-2016) in Tromsø, Norway. The total sample size included 11,152 participants after exclusions for pregnancy, extreme BMI values, or incomplete dietary data. Researchers used a validated 261-item Food Frequency Questionnaire (FFQ) to assess habitual intake over the previous year. Height and weight were measured by trained personnel using standardized scales. Food items were categorized using the NOVA classification system to determine UPF intake. A randomly selected subsample of 1,144 participants provided non-fasting blood samples for FGF21 analysis via Proximity Extension Assay technology. Statistical models controlled for age, sex, physical activity, education, smoking status, and chronic health conditions.

Key Findings

• Protein exerted a significant negative leverage on energy intake with an L-value of -0.36 (p < .001).
• Fat intake showed a positive leverage effect on total energy consumption with an L-value of 0.33 (p < .001).
• Carbohydrate proportion had no significant control over energy intake with an L-value of 0.00 (p = .966).
• The highest quintile of UPF consumption was associated with a lower protein proportion (16.4% vs 18.5% in the lowest quintile).
• UPF intake was a significant predictor of higher energy intake, showing an increase of 554 kJ per day (p < .001).
• Protein leverage was significantly stronger in low UPF consumers (L = -0.40) compared to high UPF consumers (L = -0.29, p = .009).
• FGF21 levels increased as dietary protein percentage decreased (b = -0.05, p = .001), supporting its role as a biomarker for protein restriction.

Limitations

The study relies on self-reported dietary data from an FFQ, which is susceptible to underreporting, particularly in participants with higher BMI. The cross-sectional nature prevents the establishment of a causal link between protein leverage and long term weight gain. The lack of a positive correlation between energy intake and BMI in this specific cohort limits the ability to draw conclusions regarding obesity development.

Discussion and Implications

These results confirm that the protein leverage mechanism is active in middle aged and older European adults. The data show that as the protein density of the diet drops, individuals increase their total energy intake to reach a physiological protein target. This effect is particularly relevant in the context of ultra-processed foods, which appear to dilute protein and drive passive overconsumption of fats and carbohydrates. The negative correlation between protein and FGF21 suggests a biological signaling pathway that monitors protein status and potentially modulates appetite. The positive leverage effect of fat indicates that high fat diets exacerbate the energy surplus created by protein seeking behavior.

Conclusions

Dietary protein proportion is a primary driver of total energy intake in adults, where even small dilutions in protein density lead to significant caloric overconsumption. Nutrition professionals should prioritize protein density to leverage satiety and mitigate the passive overconsumption of energy associated with ultra-processed, high fat diets.

The Effect of Including 1 Avocado Daily in a Habitual Diet on the Glycemic Index and Glycemic Load in Free-Living Adults with Overweight/Obesity

Link to study.

Abstract

High glycemic index (GI) and glycemic load (GL) diets are established drivers of type 2 diabetes and cardiovascular disease risk. While avocados are nutrient-dense and low in available carbohydrates, evidence is sparse regarding how adding a single food to a Western habitual diet influences these specific glycemic metrics over the long term. This secondary analysis of the Habitual Diet and Avocado Trial (HAT) aimed to determine if daily consumption of one large avocado (168 g) for six months would lower dietary GI and GL. The study population consisted of 1008 free-living adults with abdominal obesity (elevated waist circumference) who typically consumed fewer than two avocados per month.

The intervention group demonstrated a significant reduction in dietary GL compared to the control group. After adjusting for age, sex, BMI, energy intake, and other confounders, the avocado-supplemented group showed a reduction in dietary GL by 13.7 points (95% CI: -17.0, -10.4; p < 0.001). No significant differences were observed in dietary GI between the groups (p = 0.123 in unadjusted models; p = 0.043 when adjusted for kcal, but p = 0.063 in the fully adjusted model). Nutrient analysis revealed that the avocado group had significantly higher intakes of fiber, monounsaturated fatty acids (MUFA), and several micronutrients, alongside a lower percentage of energy from carbohydrates and animal protein.

Study Design and Methodology

This was a multicenter, randomized, parallel-arm controlled trial conducted over 26 weeks. The study enrolled 1008 adults (961 included in final analysis) with elevated waist circumference (over 35 inches for women, 40 inches for men). Participants were randomized to either the avocado group (1 large avocado/day) or the control group (habitual diet, <2 avocados/month). Dietary intake was measured using three unannounced 24-hour dietary recalls at weeks 8, 16, and 26 via the Nutrition Data System for Research (NDSR) software. The study utilized a free-living design without a metabolic ward setting or caloric restriction, providing a high degree of ecological validity. No specific nutritional counseling was provided to either group beyond avocado preparation instructions.

Key Findings

• Dietary Glycemic Load (GL) decreased by 13.7 points in the intervention group (beta = -13.73, p < 0.001).
• Total dietary fiber intake increased from 17.8 g/d to 28.0 g/d in the avocado group (p < 0.001).
• Soluble fiber intake rose significantly from 5.7 g/d to 9.3 g/d (p < 0.001).
• Monounsaturated fatty acid (MUFA) intake increased from 13.2% to 18.1% of total energy (p < 0.001).
• Carbohydrate intake as a percentage of energy decreased from 43.1% to 40.1% (p < 0.001).
• Animal protein intake was significantly lower in the avocado group (47.4 g/d vs 51.5 g/d, p = 0.003).
• Avocado consumption resulted in an 87% displacement of non-avocado carbohydrate sources.

Limitations

The study relied on self-reported 24-hour dietary recalls, which are susceptible to underreporting or memory bias. The secondary analysis nature of the study means it wasn't originally powered for GI/GL as primary endpoints. While the sample was large and diverse, the free-living design introduces potential variability in adherence that isn't present in controlled feeding trials.

Discussion and Implications

These results demonstrate that a simple, single-food addition can meaningfully alter the glycemic profile of a habitual diet without requiring complex counseling or restrictive patterns. The 14-point reduction in GL is clinically relevant, as similar magnitudes of change are associated with improved HbA1c and reduced hepatic fat in existing literature. The mechanism appears to be a combination of the avocado's low-glycemic matrix (high fiber and MUFA) and the natural displacement of higher-glycemic carbohydrate sources. This study shifts the focus from "dietary restriction" to "nutrient-dense addition" as a viable public health strategy for managing glycemic response in populations with obesity.

Conclusion

Adding one avocado daily to a habitual diet reduces dietary glycemic load by approximately 14 points, primarily through carbohydrate displacement and increased fiber intake. This single-food intervention offers a sustainable, non-restrictive method for clinicians to improve the glycemic profile of patients with overweight or obesity.

Edit: Link updated.

Marine n-3 fatty acid treatment for carotid plaques in patients with type 2 diabetes
https://doi.org/10.1186/s12933-026-03082-7

Abstract

Patients with type 2 diabetes (T2D) face a significantly elevated risk of atherosclerotic cardiovascular disease, often driven by residual dyslipidemia even when LDL cholesterol is well managed. While marine n-3 polyunsaturated fatty acids (PUFAs) are known to modulate lipid metabolism, their direct impact on subclinical atherosclerosis like carotid plaques remains controversial, especially within diabetic populations. This study addressed this gap through a 14 month double blind, randomized controlled trial involving 415 Chinese T2D patients aged 40 to 75. The primary objective was to evaluate whether high dose (3.0 g/day) or low dose (1.5 g/day) fish oil supplementation could reduce carotid plaque prevalence or improve NMR derived lipoprotein subclasses compared to a refined olive oil placebo.

The 14 month intervention showed that fish oil supplementation did not significantly reduce carotid plaque prevalence, with a p trend of 0.111. Compared to the placebo, the adjusted odds ratios (OR) for plaque risk were 1.05 (95% CI 0.51 to 2.14) for the low dose group and 0.49 (95% CI 0.23 to 1.04) for the high dose group (p trend = 0.071). No significant effects were observed for the incidence of new plaques (p trend = 0.304) or the regression of existing ones (p trend = 0.390). High dose fish oil significantly improved the lipoprotein profile, reducing remnant cholesterol (median change -14.00 mg/dL vs -0.50 mg/dL in placebo, FDR < 0.001) and lowering triglycerides across VLDL, IDL, and specific LDL/HDL subfractions. A significant gene treatment interaction was found where high dose fish oil reduced plaque risk only in patients with low genetic risk for remnant cholesterol (p interaction = 0.008, FDR = 0.056).

Study Design and Methodology

This was a 14 month, double blind, placebo controlled, randomized clinical trial (NCT03708887) conducted in China. The study enrolled 415 T2D patients who were randomized 1:1:1 into three groups: high dose (3.0 g/day EPA+DHA), low dose (1.5 g/day EPA+DHA), or a refined olive oil placebo. Researchers used carotid ultrasonography at baseline and month 14 to assess plaque status and intima media thickness (CIMT). Plasma lipoprotein subclasses were quantified using a high throughput 600.13 MHz proton NMR spectrometer. Compliance was verified via gas chromatography of plasma EPA and DHA levels and monthly capsule counts. Genetic risk scores (GRS) were constructed using Illumina Infinium Asian Screening Array genotyping to explore gene nutrient interactions.

Key Findings

• High dose fish oil reduced serum triglycerides: median change -0.405 mmol/L vs -0.085 mmol/L in placebo (p < 0.001).
• Remnant cholesterol reduction in high dose group: median change -14.00 mg/dL (FDR < 0.001).
• Adjusted OR for carotid plaque in high dose group: 0.49 (95% CI 0.23 to 1.04, p = 0.071).
• New plaque incidence OR (high dose): 0.54 (95% CI 0.22 to 1.32, p = 0.191).
• Interaction between high dose and remnant cholesterol GRS: p interaction = 0.008.
• Plaque risk reduction in low genetic risk subgroup: significant benefit observed (FDR = 0.056).
• Maximum CIMT change: -0.010 mm in high dose vs 0.000 mm in placebo (p = 0.075).

Limitations

• The 14 month duration might be too short to observe structural regression of established atherosclerotic plaques.
• The study used a mix of EPA and DHA, which may have different or opposing effects on specific LDL subfractions.
• The sample size, while large for this demographic, was underpowered to detect smaller relative risk reductions in the primary outcome.
• The population was statin naive or had low statin usage (13.5%), which limits generalizability to patients on intensive lipid lowering therapy.
• Plaque stability and composition were not assessed via advanced imaging.

Discussion and Implications

These results indicate that marine n-3 PUFA supplementation doesn't provide a broad "one size fits all" reduction in carotid atherosclerosis for T2D patients within a 14 month window. The clear, dose dependent improvement in remnant cholesterol and triglyceride rich lipoprotein subfractions confirms the metabolic efficacy of high dose fish oil, yet these biochemical shifts don't immediately translate to structural plaque changes. The discovery of a significant interaction with genetic risk for remnant cholesterol is a major step toward precision nutrition. It suggests that a patient's genetic architecture dictates their vascular response to n-3 fatty acids. Clinical practice should move away from routine supplementation for all diabetic patients and instead focus on identifying responsive subgroups through genetic and advanced lipid profiling.

Elevated remnant cholesterol is linked to non-alcoholic fatty liver disease in patients with type 2 diabetes mellitus

https://doi.org/10.3389/fnut.2026.1782646

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Abstract

Non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM) are closely interrelated metabolic conditions that significantly increase the risk of cardiovascular and hepatic complications. While traditional lipid parameters like LDL-C are standard in clinical assessments, the role of remnant cholesterol (RC), which represents the cholesterol content of triglyceride-rich lipoproteins, remains poorly defined in diabetic populations. This study aimed to investigate the independent association between RC levels and the presence of NAFLD in patients diagnosed with T2DM. The researchers utilized a cross-sectional cohort of 308 hospitalized patients to address the gap in identifying simple, cost-effective biomarkers for liver disease risk stratification in high-risk metabolic groups.

The primary analysis revealed a robust positive correlation between elevated RC and NAFLD presence. In the fully adjusted multivariable logistic regression model, each 1 mmol/L increase in RC was associated with a 4.23-fold increase in the odds of NAFLD (OR = 4.23, 95% CI: 1.73 to 12.04, p = 0.004). Restricted cubic spline analysis confirmed a linear dose-response relationship (p overall = 0.006, p nonlinear = 0.399), showing that the predicted probability of NAFLD increased from 65.7% at an RC of 0.2 mmol/L to 85.8% at 1.0 mmol/L. Sensitivity analyses using RC tertiles further validated these findings: patients in the highest tertile (T3) exhibited significantly higher odds of NAFLD compared to the lowest (T1) after full adjustment (OR = 2.05, 95% CI: 1.06 to 4.03, p trend = 0.035).

Study Design and Methodology

This cross-sectional study involved 308 patients with T2DM admitted to Hebei General Hospital between 2023 and 2025. Participants underwent an 8-hour overnight fast before venous blood collection for biochemical analysis. RC was calculated as total cholesterol minus LDL-C minus HDL-C. NAFLD was diagnosed using hepatic ultrasound imaging after strictly excluding secondary causes such as excessive alcohol consumption, viral hepatitis, or autoimmune liver disease. The statistical framework employed multivariable logistic regression across three models, with the final model adjusting for age, diabetes duration, BMI, uric acid, and albumin. Model performance was verified through the Hosmer-Lemeshow test for calibration and the DeLong method for AUC discrimination.

Key Findings

• NAFLD prevalence in the diabetic cohort: 69.0% (211 patients).
• Median RC levels in NAFLD vs. non-NAFLD: 0.6 mmol/L vs. 0.4 mmol/L (p < 0.001).
• Adjusted odds ratio (Model 3) per 1 mmol/L RC increase: 4.23 (95% CI: 1.73 to 12.04, p = 0.004).
• AUC for NAFLD discrimination using Model 3: 0.778 (95% CI: 0.723 to 0.834).
• Predicted probability of NAFLD at RC 0.8 mmol/L (75th percentile): 80.0%.
• Interaction tests for age (p = 0.772), sex (p = 0.231), and BMI (p = 0.221) showed consistent effects across all subgroups.
• Post-hoc power calculation: 99.4% power to detect the observed association.

Limitations

• The cross-sectional nature of the study prevents the determination of a causal relationship between RC and NAFLD.
• Ultrasound diagnosis lacks the precision to differentiate between simple steatosis and non-alcoholic steatohepatitis (NASH) or to quantify fibrosis stages.
• The sample size is relatively small, which may have limited the statistical power in specific subgroup analyses, such as the female population.
• Potential residual confounding exists from unmeasured variables including dietary habits, physical activity, and the use of specific lipid-lowering or antidiabetic medications.

Discussion and Implications

These findings establish remnant cholesterol as a potent and independent indicator of NAFLD risk in diabetic patients. The results support the integration of RC into standard metabolic screenings, as it provides a simple calculation from existing lipid panels without additional cost. This study shifts the focus from traditional LDL-C toward triglyceride-rich lipoprotein remnants as critical drivers of hepatic lipid accumulation and inflammation. Clinicians should recognize that even in non-obese diabetic patients, elevated RC remains a significant threat to liver health. Prioritizing RC management may offer a dual benefit in reducing both cardiovascular risk and the progression of metabolic liver disease.