This narrative review was conducted following the SANRA (Scale for the Assessment of Narrative Review Articles) guidelines (13), to ensure transparency, methodological rigor, and comprehensive reporting. Literature searches were performed in PubMed and Scopus, combining terms related to meat intake (“red meat”, “processed meat”, “unprocessed meat”, “white meat”, “poultry”) and metabolic outcomes (“type 2 diabetes”, “insulin resistance”, “glucose intolerance”) with Boolean operators adapted to each database. No date restrictions were applied. The last search was performed in August 2025. Eligible studies were prospective cohort investigations that assessed the association between meat consumption and T2DM, provided hazard ratios with corresponding confidence intervals, and reported results either stratified by sex or included an interaction term by sex. Cross-sectional studies, reviews, and articles without incidence or hazard ratio data were excluded. Only studies reporting sex-specific risk estimates were included, while those presenting pooled results without sex disaggregation were not considered, regardless of sample size or relevance. Supplementary material were reviewed when available to confirm effect estimates and subgroup definitions. Exposures were reported heterogeneously (quintiles, servings/day, cooking methods). No harmonization was attempted, in order to preserve methodological detail and avoid speculative standardization. Results are therefore presented descriptively, and direct comparisons require caution.

To avoid the risk of data duplication, special attention was given to studies based on the Nurses' Health Study (NHS, initiated in 1976), the Nurses' Health Study II (NHS II, initiated in 1989), and the Health Professionals Follow-up Study (HPFS, initiated in 1986), which are widely used in epidemiological research. Only unique risk estimates for each cohort, gender and analytical period were considered, and in cases where multiple publications reported overlapping results, the most recent or comprehensive analysis was selected. The results of these cohorts were included only if data for male and female populations were reported in the same publication, thus allowing a direct comparison by sex under consistent methodological conditions. Single-sex cohorts published in isolation, without a counterpart of the opposite sex in the same study, were not considered in order to avoid indirect or methodologically inconsistent comparisons.

Exposures were classified according to conventional epidemiological definitions. Total meat (TM) included the sum of red (RM), processed (PM), and white meat (WM). RM (red meat) refers specifically to unprocessed beef, pork, lamb, or veal, while PM (processed meat) includes products preserved by smoking, curing, salting, or the addition of chemical preservatives (such as sausages, salami, ham, bacon, and hot dogs). WM refers to unprocessed poultry, particularly chicken and turkey. When original studies reported “total red meat” (TRM), including both processed and unprocessed forms, this was explicitly indicated in the tables and figures.

For each study that met the inclusion criteria, details regarding the author, year, country, population characteristics, meat exposure, results, follow-up duration, sex-specific risk ratios with confidence intervals, and overall interpretation of the association were collected and verified. The study selection process is illustrated in Figure 1, and methodological details are summarized in Supplementary Table S1, including exposure contrasts, covariates in the fully adjusted models, and effect measure type (odds ratio or hazard ratio). Given the narrative nature of this review, hazard ratios derived from heterogeneous exposure contrasts (e.g., quintiles, servings/day, 50–100 g/day, or cooking frequency) were reported as originally presented, without harmonization or dose re-expression. This decision preserved methodological integrity and avoided speculative standardization, but necessarily reduces comparability across studies. Multiple publications analyzing the NHS I, NHS II, and HPFS cohorts were included because they addressed distinct methodological questions: Pan et al. examined associations fully adjusted for BMI; Gu et al. excluded BMI to explore mediation by adiposity; and Liu et al. focused on cooking methods and doneness levels, adjusting for total meat intake and BMI. These were treated as complementary perspectives rather than independent confirmations. Although only studies reporting sex-specific estimates were included, formal interaction tests between sex and meat intake were rarely performed. Reported HRs and 95% CIs were therefore considered as presented; in some cases, overlap of CIs between sexes was noted as a cautious indicator. However, no pooled meta-regression or formal interaction analysis was attempted, in line with the qualitative objectives of this review. Consequently, the results should be interpreted as exploratory, anchored to study-specific exposure definitions and adjustment sets, and not as confirmatory evidence of sex-based effect modification.

Several otherwise relevant prospective cohort studies were excluded because they did not report hazard ratios stratified by sex or did not include an interaction term. This choice was deliberate to ensure comparability of gender-specific estimates across studies.

The link between meat consumption and type 2 diabetes remains a central theme in nutritional epidemiology. Data collected from large cohorts in Europe, the United States and Asia indicate that high consumption of red and processed meat is generally associated with an increased risk of type 2 diabetes, while the results for white meat are weaker and less consistent (14–16). A recent federated meta-analysis, which brought together nearly two million individuals from 31 cohort studies, confirmed a modest but significant increase in diabetes risk across all meat categories (17). Within this general picture, some prospective cohort studies suggest possible sex-specific differences, notably stronger associations for processed and red meat in men and more variable patterns for white meat in women. However, aggregate analyses based on individual participant data found no statistical evidence of interaction between the sexes (17). This underscores the importance of interpreting observed differences between the sexes as signals rather than definitive conclusions, while recognizing potential biological and behavioral explanations, such as differences in hormonal profiles, fat distribution, iron metabolism, and dietary habits. Regional dietary cultures also influence these associations. In Asian populations, where red meat consumption is relatively low and diets include more fish and soy products, the reported associations with diabetes risk are often weaker and sometimes vary by sex (18). In contrast, cohorts from Europe and North America, characterized by higher consumption of red and processed meat and lower consumption of protective plant foods, tend to show stronger and more consistent associations, especially in men. These cultural and dietary contexts provide an important background for understanding where and why gender-specific risk patterns may emerge. Overall, current evidence suggests that meat consumption contributes to the risk of type 2 diabetes in different populations, with possible but as yet unconfirmed differences between men and women. Exploring these patterns in relation to both biological mechanisms and regional dietary environments remains a key direction for future research.

Several biological mechanisms may explain the stronger associations observed in men. Estrogen exerts insulin-sensitizing and anti-inflammatory effects, providing some metabolic protection in premenopausal women (19). In contrast, men, with lower estrogen levels, may be more sensitive to the pro-inflammatory effects of nitrates, nitrites, haem iron and AGEs present in processed meats (20–22). Men also tend to have more visceral adipose tissue and greater iron stores, both of which are linked to increased insulin resistance and oxidative stress (23, 24). Men often consume larger portions of RM and PM and fewer protective foods such as vegetables and legumes. This dietary pattern increases exposure to harmful compounds and reduces antioxidant intake (25). Social and cultural gender roles may reinforce these patterns, as demonstrated by the HELIUS (18) and Swiss cohort (19) studies, potentially increasing metabolic risk. Cultural norms also shape gendered eating behaviors, with men in Western settings more likely to consume larger portions of red and processed meat, whereas women often report higher poultry intake. These sociocultural factors may reinforce biological susceptibilities, contributing to the heterogeneous risk patterns observed across regions.

RM contributes to the intake of heme iron, which can promote oxidative stress and beta cell dysfunction, particularly in men (26). It also affects circulating metabolomic profiles, increasing BCAAs and reducing glycine, both of which may be linked to insulin resistance (27). In addition, RM is a dietary source of L-carnitine, which is converted to TMAO, a compound that may be associated with inflammation and metabolic dysfunction (28, 29). Heterocyclic amines (HCAs), such as PhIP and harman, are mutagenic compounds formed during high-temperature cooking of red and processed meats, particularly grilling and frying (30). These compounds can impair insulin signaling by increasing hepatic glucose production, interfering with insulin-induced AKT phosphorylation, and promoting gluconeogenic gene expression (31, 32). Men may be more susceptible to these effects due to lower estrogen levels and genetic differences in HCA metabolism, especially involving N-acetyltransferase 2 (NAT2) polymorphisms, which modulate the detoxification capacity and influence metabolic response to HCAs (33). A Finnish cohort study showed that partial replacement of red or processed meat with plant-based foods moderately reduced the risk of T2DM (HR = 0.97, 95% CI: 0.95–1.00) (34). White meat shows less consistent associations with T2DM risk. In this review, HR > 1.0 was reported more frequently in women. European studies such as EPIC-InterAct (35) and Ericson et al. (36) found modest associations in women, while studies reported no or inverse associations, especially in men (37, 38).

The interpretation of these results must take into account important limitations. First, the evidence derives mainly from observational cohort studies, which precludes causal inference and may be affected by residual confounding from lifestyle, socioeconomic, or dietary factors not fully captured in adjusted models. Second, the number of studies reporting sex-specific estimates remains limited, reducing statistical power and restricting generalisability, especially beyond Western and Asian populations where most cohorts were conducted. Third, exposures were defined heterogeneously (e.g., intake quintiles, daily servings, cooking frequency), complicating direct comparisons and partly explaining inconsistencies between cohorts. For this reason, no harmonization or dose re-expression was attempted, as such procedures would require unverifiable assumptions and loss of methodological detail. Another major limitation is the absence of formal sex-interaction tests in most primary studies. Reported differences were generally inferred from hazard ratios, an indirect approach that may either over- or underestimate effect modification. These signals should therefore be regarded as exploratory and hypothesis-generating rather than confirmatory. Finally, although estimates were extracted directly from the original studies, differences in exposure contrasts and covariate adjustments could lead to misinterpretation. To mitigate this, uniform annotation of contrast units, adjustment models, and significance thresholds was added to all tables and figures. In this context, the present review should be viewed as complementary to the federated meta-analysis by Li et al. (17), which found no overall sex interaction. Whereas pooled analyses provide robust standardized estimates, this narrative synthesis preserves study-specific contrasts and highlights potential sex-specific patterns and biological plausibility, pointing to areas where future pooled analyses with pre-specified interaction testing are needed.