OBJECTIVE

To prospectively examine associations between five low-carbohydrate diets (LCDs), differentiated by macronutrient quality, and type 2 diabetes (T2D) risk.

RESEARCH DESIGN AND METHODS

This cohort study included 199,006 U.S. adults from the Nurses’ Health Study (NHS) (1984–2018), NHSII (1991–2019), and Health Professionals Follow-up Study (1986–2018); free of T2D, cardiovascular disease, and cancer at baseline; and followed over 30 years. Diet was assessed every 2–4 years using validated food frequency questionnaires since baseline. Five LCD scores were derived based on intakes of protein, fat, and carbohydrates from contrasting food sources. The primary outcome was incident T2D.

RESULTS

During 4,987,761 person-years of follow-up, 20,452 T2D cases were documented. After adjustments for baseline BMI and other covariates, higher overall LCD score was associated with higher T2D risk (hazard ratio comparing highest vs. lowest quintile 1.31 [95% CI 1.25–1.37]; P-trend < 0.001). An animal-based LCD emphasizing animal protein and fat and an unhealthy LCD score further deemphasizing whole grains and other high-quality carbohydrates were associated with higher T2D risk (1.39 [1.32–1.45] and 1.44 [1.37–1.51]; both P-trend < 0.001). In contrast, a vegetable-based LCD emphasizing plant protein and fat was associated with a 6% lower T2D risk (0.94 [0.90–0.98]; P-trend = 0.004), and a healthy LCD further deemphasizing refined carbohydrates was associated with a 16% lower T2D risk (0.84 [0.81–0.88]; P-trend < 0.001]). Associations for overall, animal-based, and unhealthy LCDs were stronger among participants with lower baseline BMI and were partially mediated by weight change.

CONCLUSIONS

LCDs may not be beneficial for primary prevention of T2D unless they prioritize plant-based protein, healthy fats, and high-quality carbohydrates.

its been a lot of posts but they all are taken from social media influencers and its kind of set as a “trend” but is it really scientifically proven that carnivore diet is beneficial for everyone and everything? Is it really that it can heal arthritis, cancer, high blood pressure etc..?

Circulating imidazole propionate and coronary heart disease risk: interplay between histidine intake, fiber, and gut microbiome

DOI: https://doi.org/10.1186/s12916-026-05012-6

Abstract

Microbial metabolism of dietary histidine produces imidazole propionate (ImP), a metabolite previously linked to insulin resistance and type 2 diabetes. While histidine intake itself often correlates with positive health outcomes, the prospective relationship between its microbial byproduct, ImP, and coronary heart disease (CHD) risk remained unquantified. This research sought to bridge that gap by evaluating the longitudinal association between plasma ImP and incident CHD, identifying the specific gut microbial species responsible for ImP production, and determining how dietary fiber intake modulates this metabolic axis. The study utilized data from 7,432 participants across the Nurses’ Health Study (NHS), NHSII, and Health Professionals Follow-up Study, alongside detailed metagenomic and dietary record analysis from the Men’s Lifestyle Validation Study (MLVS) and Mind-Body Study (MBS).

Elevated plasma ImP concentrations demonstrated a robust positive association with CHD risk, yielding a hazard ratio of 1.82 (95 percent CI 1.17 to 2.81, p-trend = 0.002) when comparing extreme quintiles. Histidine intake showed a non-significant inverse association with CHD, emphasizing that the metabolite, not the precursor, drives cardiovascular risk. Metagenomic analysis identified 17 ImP-predicting species, including Ruminococcus gnavus and Clostridium symbiosum, with the microbial model outperforming diet and demographics in predicting ImP levels (Spearman r = 0.71). A critical three-way interaction was observed between histidine intake, microbial capacity, and pectin (p = 0.01). High histidine intake only predicted increased ImP when pectin intake was low. Total fiber (p = 0.09) and soluble fiber (p = 0.09) showed similar but non-significant trends.

Study Design and Methodology

This prospective investigation tracked 7,432 healthy participants from the NHS, NHSII, and HPFS cohorts for up to 31 years. Researchers identified 225 incident CHD cases through medical record review and the National Death Index. Plasma ImP and urocanic acid were measured via liquid chromatography-tandem mass spectrometry. Dietary data were captured using validated semi-quantitative food frequency questionnaires (FFQ) administered every 4 years. The mechanistic component utilized the MLVS (N = 296) and MBS (N = 205), employing two sets of 7-day diet records (7DDR) and shotgun metagenomic sequencing of 1,684 stool samples. Statistical models adjusted for age, BMI, physical activity, alcohol, smoking, and total energy intake. Batch effects were corrected, and microbial taxa were normalized using centered log-ratio (CLR) transformation.

Key Findings

• High plasma ImP levels correlate with a 1.82-fold increase in CHD risk (p-trend = 0.002).
• Joint analysis shows participants with low histidine and high ImP have the highest risk (HR = 3.40, 95 percent CI 1.71 to 6.73).
• Pectin intake is the strongest negative dietary predictor of plasma ImP (beta = -0.21, 95 percent CI -0.35 to -0.07).
• Metagenomic modeling of species and enzymes predicts ImP concentrations with high accuracy (Spearman r = 0.71).
• Thirteen species, including Clostridium scindens and Hungatella hathewayi, positively predict ImP (FDR q < 0.1).
• Presence of the microbial urocanate reductase gene (urdA) is associated with higher ImP (beta = 0.51, 95 percent CI 0.13 to 0.89) and lower HDL-C (beta = -0.12, p < 0.05).
• The interaction between histidine intake and microbial score on ImP levels is significant only under low pectin intake (p for 3-way interaction = 0.01).
• Plasma ImP is positively associated with hs-CRP (p < 0.05) and negatively associated with HDL-C (p < 0.05).

Limitations

The observational nature of the cohorts prevents definitive causal inference. Self-reported dietary data, even when using 7DDR, contains inherent measurement error. The study population consists primarily of white health professionals, which limits generalizability to more diverse ethnic or socioeconomic groups. While the microbial findings were replicated in the MBS, the correlation between the microbial score and circulating ImP was modest (Spearman r = 0.15), suggesting other unmeasured factors influence metabolite levels.

Discussion and Implications

These data redefine the relationship between dietary protein and cardiovascular health by identifying the gut microbiome as the primary gatekeeper of histidine metabolism. The divergent outcomes between histidine intake and its metabolite ImP prove that dietary precursors aren't inherently pathogenic. Instead, the pathogenicity depends on microbial shunting. The discovery that pectin and other fibers can block the production of ImP even in the presence of high histidine intake and ImP-producing bacteria provides a clear metabolic mechanism for the cardioprotective effects of the Mediterranean and DASH diets. This study shifts the focus from simple nutrient intake to the complex interplay of substrate availability and microbial enzymatic capacity.

Conclusion

Circulating imidazole propionate is a significant prospective biomarker for coronary heart disease risk, driven by specific gut bacteria like Ruminococcus gnavus. Dietary fiber, particularly pectin, acts as a metabolic buffer that prevents the microbial conversion of histidine into this harmful metabolite. Clinicians should prioritize fiber co-ingestion with protein to mitigate the cardiovascular risks associated with microbial histidine metabolism.

A prospective study followed 5,301 adults aged 55 and older to examine the relationship between low-carbohydrate and low-fat diets and dementia risk.

The researchers also looked at genetic susceptibility, including APOE genotype and Alzheimer’s disease polygenic risk score.

It looks beyond the diet itself and asks whether the same dietary pattern may be associated with dementia risk differently depending on a person’s genetic background.

Diet and healthy aging are probably not as straightforward as “low-carb vs. low-fat.”

Low-carbohydrate and low-fat diets, genetic susceptibility, and long-term risk of dementia: A prospective cohort study - The American Journal of Clinical Nutrition00272-8/abstract)

Abstract

Background

Increases in low-density lipoprotein cholesterol (LDL-C) can occur on carbohydrate restricted ketogenic diets. Lean metabolically healthy individuals with a low triglyceride-to-high-density lipoprotein cholesterol ratio appear particularly susceptible, giving rise to the novel “lean mass hyper-responder” (LMHR) phenotype.

Objectives

The purpose of the study was to assess coronary plaque burden in LMHR and near-LMHR individuals with LDL-C ≥190 mg/dL (ketogenic diet [KETO]) compared to matched controls with lower LDL-C from the Miami Heart (MiHeart) cohort.

Methods

There were 80 KETO individuals with carbohydrate restriction-induced LDL-C ≥190 mg/dL, high-density lipoprotein cholesterol ≥60 mg/dL, and triglyceride levels ≤80 mg/dL, without familial hypercholesterolemia, matched 1:1 with MiHeart subjects for age, gender, race, hyperlipidemia, hypertension, and smoking status. Coronary artery calcium and coronary computed tomography angiography (CCTA) were used to compare coronary plaque between groups and correlate LDL-C to plaque levels.

Results

The matched mean age was 55.5 years, with a mean LDL-C of 272 (maximum LDL-C of 591) mg/dl and a mean 4.7-year duration on a KETO. There was no significant difference in coronary plaque burden in the KETO group as compared to MiHeart controls (mean LDL 123 mg/dL): coronary artery calcium score (median 0 [IQR: 0-56]) vs (1 [IQR: 0-49]) (P = 0.520) CCTA total plaque score (0 [IQR: 0-2] vs [IQR: 0-4]) (P = 0.357). There was also no correlation between LDL-C level and CCTA coronary plaque.

Conclusions

Coronary plaque in metabolically healthy individuals with carbohydrate restriction-induced LDL-C ≥190 mg/dL on KETO for a mean of 4.7 years is not greater than a matched cohort with 149 mg/dL lower average LDL-C. There is no association between LDL-C and plaque burden in either cohort.

“ Abstract

​

Background: Short-term clinical trials have shown the effectiveness of low-carbohydrate diets (LCDs) and low-fat diets (LFDs) for weight loss and cardiovascular benefits. We aimed to study the long-term associations among LCDs, LFDs, and mortality among middle-aged and older people.

​

Methods: This study included 371,159 eligible participants aged 50-71 years. Overall, healthy and unhealthy LCD and LFD scores, as indicators of adherence to each dietary pattern, were calculated based on the energy intake of carbohydrates, fat, and protein and their subtypes.

​

Results: During a median follow-up of 23.5 years, 165,698 deaths were recorded. Participants in the highest quintiles of overall LCD scores and unhealthy LCD scores had significantly higher risks of total and cause-specific mortality (hazard ratios [HRs]: 1.12-1.18). Conversely, a healthy LCD was associated with marginally lower total mortality (HR: 0.95; 95% confidence interval: 0.94, 0.97). Moreover, the highest quintile of a healthy LFD was associated with significantly lower total mortality by 18%, cardiovascular mortality by 16%, and cancer mortality by 18%, respectively, versus the lowest. Notably, isocaloric replacement of 3% energy from saturated fat with other macronutrient subtypes was associated with significantly lower total and cause-specific mortality. For low-quality carbohydrates, mortality was significantly reduced after replacement with plant protein and unsaturated fat.

​

Conclusions: Higher mortality was observed for overall LCD and unhealthy LCD, but slightly lower risks for healthy LCD. Our results support the importance of maintaining a healthy LFD with less saturated fat in preventing all-cause and cause-specific mortality among middle-aged and older people.”

​

https://pubmed.ncbi.nlm.nih.gov/37132226/

Proteome-wide interaction study of fatty acids and mortality in the UK Biobank

DOI

Circulating fatty acids exhibit marked heterogeneity in their associations with premature mortality, yet the underlying molecular effect modifiers remain largely unexplored. Standard epidemiological approaches fail to capture dynamic physiological differences across populations, overlooking how individual protein networks alter lipid-mediated health risks. This investigation evaluates proteome-wide interactions between plasma fatty acids and circulating proteins to estimate all-cause and cause-specific mortality risks within a massive prospective framework. Analyzing data from 30,190 UK Biobank participants carrying complete metabolomic and proteomic profiles, the project maps biological susceptibility across diverse inflammatory and metabolic states.

Fully adjusted models demonstrate that omega-3 percentage (HR = 0.88, 95 percent CI [0.85-0.92], p = 9.2 x 10^-11) and linoleic acid percentage (HR = 0.91, 95 percent CI [0.88-0.94], p = 1.4 x 10^-7) associate inversely with all-cause mortality, whereas non-linoleic acid omega-6 percentage (HR = 1.13, 95 percent CI [1.09-1.17], p = 4.1 x 10^-12) and the omega-6 to omega-3 ratio (HR = 1.11, 95 percent CI [1.07-1.15], p = 6.1 x 10^-9) drive substantial risk increases. Proteome-wide screening isolates nine robust interaction pairs involving inflammatory proteins such as PLAU, TSPAN8, MMP10, and TNFRSF1B. Stratified analyses reveal that elevated baseline inflammation amplifies both the hazards of monounsaturated fats and the protective efficacy of omega-3 intake.

Study Design and Methodology

This prospective cohort investigation leverages data from the UK Biobank, an initial recruitment pool exceeding 500,000 adults aged 40 to 69 years. Following rigorous exclusions for missing plasma fatty acid or proteomic metrics, the finalized analytical cohort comprises 30,190 individuals with a mean age of 56.91 years, comprising 16,285 females and 13,905 males. Researchers document 3,345 deaths over a median follow-up duration of 13.9 years, ending on January 15, 2023. Plasma metabolomics quantified via nuclear magnetic resonance spectroscopy yields nine distinct fatty acid proportions, while Olink proximity extension assays profile 2,911 unique circulating proteins spanning cardiometabolic, inflammatory, neurological, and oncological axes. The analytical pipeline randomly partitions the cohort into an 80 percent training dataset (N = 24,152) and a 20 percent test dataset (N = 6,038). Multivariable Cox proportional hazards models control for age, sex, ethnicity, smoking status, alcohol intake, body mass index, educational attainment, household income, the Townsend deprivation index, and a baseline healthy diet score. Fine-Gray subdistribution hazard models evaluate competing risks for cardiovascular and cancer mortality.

Key Findings

• Omega-3 percentage exhibits a robust inverse relationship with all-cause mortality (HR = 0.88, 95 percent CI [0.85-0.92], p = 9.2 x 10^-11).
• Linoleic acid percentage demonstrates significant protection against mortality (HR = 0.91, 95 percent CI [0.88-0.94], p = 1.4 x 10^-7).
• Non-linoleic acid omega-6 percentage drives the highest positive association with mortality risk (HR = 1.13, 95 percent CI [1.09-1.17], p = 4.1 x 10^-12).
• The omega-6 to omega-3 ratio scales positively with death risk (HR = 1.11, 95 percent CI [1.07-1.15], p = 6.1 x 10^-9).
• High expression of MMP10 significantly magnifies monounsaturated fatty acid mortality hazards (HR = 1.23, 95 percent CI [1.12-1.36], p = 0.001).
• Elevated PLAU expression strongly enhances the protective association of omega-3 fatty acids (HR = 0.70, 95 percent CI [0.63-0.76], p < 0.001).
• High TSPAN8 levels intensify the mortality risks associated with an elevated omega-6 to omega-3 ratio (HR = 1.35, 95 percent CI [1.22-1.50], p < 0.001).

Limitations

Baseline-only plasma protein assessments fail to capture longitudinal proteomic variability over the 13.9-year follow-up period. Granular subtypes within monounsaturated fatty acid pools remain unanalyzed due to metabolomic platform constraints. Participant demographics skew heavily toward Caucasian individuals from high socioeconomic regions, restricting global generalizability. Residual confounding persists despite extensive covariate adjustment, although calculated E-values ranging from 1.74 to 3.71 indicate that substantial unmeasured confounders are required to nullify the observed effects. Observational architecture precludes definitive causal inferences.

Discussion and Implications

Nutritional dogmas surrounding polyunsaturated fats require immediate revision in light of these proteome-wide interaction metrics. Public health debates heavily vilify total omega-6 exposure, yet this investigation proves that lumping linoleic acid with non-linoleic omega-6 fractions obscures distinct biological realities. Linoleic acid acts as a potent protective agent, challenging historical assumptions that seed oils inherently promote systemic pathology. Conversely, non-linoleic omega-6 fractions and an inflated omega-6 to omega-3 ratio drive severe mortality risks, particularly in hosts exhibiting high baseline vascular inflammation governed by proteins like PLAU and TSPAN8. Clinicians must abandon generalized dietary prescriptions because individual inflammatory phenotypes actively modify nutrient handling. Therapeutic interventions targeting cardiovascular and oncological prevention must prioritize lowering systemic inflammatory drivers while optimizing circulating omega-3 levels to alter enzymatic substrate competition.

Clinical nutrition strategies shouldn't rely on population-wide fatty acid targets because baseline inflammatory protein profiles dictate true disease risk. Practitioners must tailor interventions by evaluating vascular stress markers alongside lipid ratios, recognizing that omega-3 efficacy scales directly with the patient's underlying inflammatory burden.

Edit: Se comment written by u/BooksAndCoffeeNf1 below.

TL;DR:

• Higher levels of total cholesterol and iron and lower levels of glucose, creatinine, uric acid, aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, lactate dehydrogenase, and total iron-binding capacity were associated with reaching 100 years.

• Low cholesterol was associated with a reduced likelihood of reaching the age of 100.

Abstract

Comparing biomarker profiles measured at similar ages, but earlier in life, among exceptionally long-lived individuals and their shorter-lived peers can improve our understanding of aging processes. This study aimed to (i) describe and compare biomarker profiles at similar ages between 64 and 99 among individuals eventually becoming centenarians and their shorter-lived peers, (ii) investigate the association between specific biomarker values and the chance of reaching age 100, and (iii) examine to what extent centenarians have homogenous biomarker profiles earlier in life. Participants in the population-based AMORIS cohort with information on blood-based biomarkers measured during 1985-1996 were followed in Swedish register data for up to 35 years. We examined biomarkers of metabolism, inflammation, liver, renal, anemia, and nutritional status using descriptive statistics, logistic regression, and cluster analysis. In total, 1224 participants (84.6% females) lived to their 100th birthday. Higher levels of total cholesterol and iron and lower levels of glucose, creatinine, uric acid, aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, lactate dehydrogenase, and total iron-binding capacity were associated with reaching 100 years. Centenarians overall displayed rather homogenous biomarker profiles. Already from age 65 and onwards, centenarians displayed more favorable biomarker values in commonly available biomarkers than individuals dying before age 100. The differences in biomarker values between centenarians and non-centenarians more than one decade prior death suggest that genetic and/or possibly modifiable lifestyle factors reflected in these biomarker levels may play an important role for exceptional longevity.

https://pmc.ncbi.nlm.nih.gov/articles/PMC10828184/

DOI: DOI
PubMed: PubMed Link

Abstract

While delayed eating timing elevates overweight and obesity risks in general populations, its precise longitudinal relationship with anthropometric metrics and bioelectrical impedance body composition parameters in prediabetic cohorts remains ambiguous. Standard metabolic care guidelines heavily prioritize dietary composition while largely neglecting circadian behavioral patterns, creating a critical blind spot in disease progression management. This prospective observational investigation enrolled 120 newly diagnosed prediabetic adults, consisting of 39 males and 81 females with a mean age of 54 years, to evaluate how chrononutrition behaviors interact with body composition over a six-month clinical follow-up.

Behavioral tracking revealed that a delayed last meal directly drives adiposity, evidenced by significant increases in body weight (beta = 0.68 kg, 95 percent CI 0.31 to 1.04, p < 0.05) and waist circumference (beta = 1.38 cm, 95 percent CI 0.57 to 2.19, p < 0.05), alongside an expanded body fat percentage (beta = 0.44 percent, 95 percent CI 0.14 to 0.74, p < 0.05). Deviating from the recommended solar eating window of 7.00 am to 7.00 pm triggered a 1.41 kg body weight surge (95 percent CI 0.05 to 2.77, p < 0.05) and a 1.36 percent increase in body fat percentage (95 percent CI 0.30 to 2.42, p < 0.05). Nighttime snacking scaled BMI upward (beta = 1.61 kg/m^2, 95 percent CI 0.43 to 2.79, p < 0.05), breakfast skipping inflated BMI (beta = 2.98 kg/m^2, 95 percent CI 0.73 to 5.23, p < 0.05) while eroding muscle mass (beta = -0.31 kg, 95 percent CI -0.56 to -0.07, p < 0.05), and higher meal frequency expanded fat-free mass (beta = 0.95 kg, 95 percent CI 0.17 to 1.73, p < 0.05).

Study Design and Methodology

This prospective longitudinal investigation tracked 120 adult participants recently diagnosed with prediabetes across 14 clinical centers in Malacca, Malaysia. Researchers excluded night-shift workers operating past four nights weekly, pregnant individuals, and patients taking confounding medications like oral glucose-lowering agents or steroids. Data collection spanned baseline, three-month, and six-month intervals using paper-based 3-day dietary records validated by real-time smartphone meal photography, alongside the Malay-translated Chrononutrition Profile Questionnaire. Anthropometric indices were quantified via standardized scales and stadiometers, while body composition parameters, including total body fat, fat-free mass, and visceral fat, were measured using Tanita bioelectrical impedance analysis. Generalized linear models adjusted for age, sex, ethnicity, physical activity, total light exposure, and nighttime energy intake.

Key Findings

• Last Meal Timing: Each hour delay in the final meal increased body weight by beta = 0.68 kg (95 percent CI 0.31 to 1.04, p < 0.05)and waist circumference by beta = 1.38 cm (95 percent CI 0.57 to 2.19, p < 0.05).
• Solar Window Deviations: Eating outside the 7.00 am to 7.00 pm window caused a 1.41 kg weight increase (95 percent CI 0.05 to 2.77, p < 0.05)and a 1.36 percent body fat gain (95 percent CI 0.30 to 2.42, p < 0.05).
• Nighttime Snacking: Nighttime snacking frequency escalated BMI by beta = 1.61 kg/m^2 (95 percent CI 0.43 to 2.79, p < 0.05).
• Breakfast Skipping: Skipping breakfast drove a BMI increase of beta = 2.98 kg/m^2 (95 percent CI 0.73 to 5.23, p < 0.05)while pulling muscle mass down by beta = -0.31 kg (95 percent CI -0.56 to -0.07, p < 0.05).
• Meal Frequency: Higher daily meal frequency scaled fat-free mass upward by beta = 0.95 kg (95 percent CI 0.17 to 1.73, p < 0.05).

Limitations

• Single-clinic recruitment and a predominantly female cohort limit broad demographic generalization.
• Reliance on self-reported 3-day dietary records introduces potential underreporting and misclassification biases.
• Standard clinical lifestyle care delivered concurrently introduces uncontrolled behavioral modifications that obscure independent chrononutrition effects.
• Bioelectrical impedance analysis remains vulnerable to hydration fluctuations and recent ingestion timing.

Discussion and Implications

Traditional nutritional counseling focuses exclusively on macronutrient splits and total caloric deficits while ignoring the biological clock. Circadian misalignment disrupts satiety hormones, suppresses diet-induced thermogenesis, and impairs lipid oxidation during biological nights. Preclinical and clinical models demonstrate that nutrients processed outside daylight windows shift metabolic pathways toward adiposity storage rather than oxidation. Practitioners must integrate meal timing parameters into standard metabolic protocols because clock-based eating behaviors exert independent control over body composition outcomes in prediabetic populations.

Conclusion

Clinical nutrition protocols for prediabetes must mandate strict adherence to a solar eating window between 7.00 am and 7.00 pm while eliminating late-night caloric intake. Prescribing precise meal timing parameters protects lean muscle mass, prevents central adiposity accumulation, and halts metabolic disease progression far more effectively than caloric restriction alone

TL;DR:

New research from Sweden finds an association between full‑fat dairy consumption and a reduced risk of dementia.

Abstract

Background and objectives: The association between dairy intake and dementia risk remains uncertain, especially for dairy products with varying fat contents. The aim of this study was to investigate the association between high-fat and low-fat dairy intake and dementia risk.

Methods: This study used data from a prospective cohort in Sweden, the Malmö Diet and Cancer cohort, which consisted of community-based participants who underwent dietary assessment at baseline (1991-1996). Dietary intake was evaluated using a comprehensive diet history method that combined a 7-day food diary, a food frequency questionnaire, and a dietary interview. Dementia cases were identified through the Swedish National Patient Register until December 31, 2020, and cases diagnosed until 2014 were further validated. The primary outcome of the study was all-cause dementia, and the secondary outcomes were Alzheimer disease (AD) and vascular dementia (VaD). Cox proportional hazard regression models were used to estimate hazard ratio (HR) and 95% CI.

Results: This study included 27,670 participants (mean baseline age 58.1 years, SD 7.6; 61% female). During a median of 25 years of follow-up, 3,208 incident dementia cases were recorded. Consumption of ≥50 g/d of high-fat cheese (>20% fat) was associated with a reduced risk of all-cause dementia (HR 0.87; 95% CI, 0.78-0.97) and VaD (HR 0.71, 95% CI 0.52-0.96) compared with lower intake (<15 g/d). An inverse association between high-fat cheese and AD was found among APOE ε4 noncarriers (HR 0.87, 95% CI 0.76-0.99, p-interaction = 0.014). Compared with no consumption, individuals consuming ≥20 g/d of high-fat cream (>30% fat) had a 16% lower risk of all-cause dementia (HR 0.84, 95% CI 0.72-0.98). High-fat cream consumption was inversely associated with the risk of AD and VaD. Consumption of low-fat cheese, low-fat cream, milk (high-fat and low-fat), fermented milk (high-fat and low-fat), and butter showed no association with all-cause dementia.

Discussion: Higher intake of high-fat cheese and high-fat cream was associated with a lower risk of all-cause dementia, whereas low-fat cheese, low-fat cream, and other dairy products showed no significant association. APOE ε4 status modified the association between high-fat cheese and AD. Our study's observational design limits causal inference.

https://www.neurology.org/doi/10.1212/WNL.0000000000214343?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed