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Sex differences in diabetes management have gained increasing attention in recent years, with multiple studies highlighting significant sex-related variations in insulin sensitivity and glucose metabolism. Fluctuations in sex hormones, such as estrogen and progesterone, significantly influence insulin sensitivity. Additionally, the menstrual cycle has been shown to impact glucose metabolism and other medical disorders. Differences in body fat distribution play a role in diabetes management, with females generally having more subcutaneous fat and males more visceral fat. Visceral fat is closely associated with increased insulin resistance. These biological differences may contribute to variations in diabetes treatment outcomes between males and females. Apart from all the biological factors, psychosocial factors such as variations in health-seeking behaviors, adherence to treatment, and access to care may further influence treatment outcomes. Females generally demonstrate higher engagement with health information and self-care behaviors, which may lead to better adherence to treatment plans, such as consistent glucose monitoring and insulin administration. However, mental health challenges, including higher rates of anxiety and depression in females, can negatively impact diabetes management by increasing stress levels, which are known to worsen glycemic control.

According to the definition declared in Sex and Gender Equity in Research (SAGER) guidelines, gender refers to the socially constructed roles, behaviors, and identities of female, male, and gender-diverse people; sex refers to a set of biological attributes in humans and animals that are associated with physical and physiological features including chromosomes, gene expression, hormone function, and reproductive/sexual anatomy. In this paper, we use the term ‘sex’ to refer to biological differences. While sex-specific health differences, such as those observed in gestational diabetes where pregnancy-related hormonal changes impact diabetes management, have been more extensively studied, the impact of sex on glycemic outcomes in type 1 diabetes and type 2 diabetes remains inadequately explored. Existing studies often lack sufficient power or fail to stratify data by sex, leaving a critical knowledge gap. Understanding these differences is essential to optimizing personalized diabetes care and achieving equitable health outcomes.

Early research prior to 2014 had already indicated potential sex differences in diabetes management. For instance, Nilsson et al found that elderly male patients with diabetes exhibited more favorable risk factor control compared with females. Legato et al reported that women with type 2 diabetes often required higher daily insulin doses than men to achieve similar glycated hemoglobin (HbA1c) targets, although the study did not thoroughly explore downstream consequences such as hypoglycemia. Additionally, Franconi et al demonstrated that estrogens influence glucose metabolism and suggested that estrogen receptor α might protect pancreatic β-cells from apoptosis, providing a biological basis for sex differences in the development of type 1 diabetes. While these early findings laid important groundwork, subsequent research over the last decade has sought to deepen understanding of sex-specific factors affecting glycemic outcomes, which this systematic review aims to synthesize. By critically analyzing the available literature, we would like to answer how sex-specific factors influence treatment effectiveness in diabetes management, and how this understanding can contribute to the development of more personalized approaches.

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The 2020 PRISMA checklist is provided in . The protocol was prospectively registered in PROSPERO (registration ID: CRD420251144696 (available at: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251144696 )). The entire review process followed the registered protocol without substantive amendments; minor pre-analysis clarifications did not change eligibility criteria or synthesis decisions. The research question was defined using Participants, Intervention, Comparison, Outcomes, Study design criteria. A computerized search of PubMed, Scopus, the Cochrane Library, and Google Scholar (as a supplementary source) was performed to identify studies comparing sex-specific glycemic outcomes in people with diabetes treated with insulin. Searches covered publications from August 1, 2014 to December 31, 2025. The search was last updated on January 10, 2026. Search terms included combinations of: “diabetes,” “type 1 diabetes,” “type 2 diabetes,” “CSII,” “insulin pump,” “MDI,” “multiple daily injections,” “automated insulin delivery,” “AID,” “insulin therapy,” “sex differences,” “gender differences,” “HbA1c,” “blood glucose,” “time in range,” “glycemic control,” “hypoglycemia,” “glycemia.” Terms with similar meanings were combined with “OR” and grouped with parentheses, then linked with other groups using “AND.” Detailed search strings for each database are provided in . Citation lists of included articles were also screened to capture additional studies.

Search results were combined and de-duplicated using Rayyan software. Titles and abstracts were screened, followed by full-text review against eligibility criteria. Two reviewers independently extracted data using a standardized form in Microsoft Excel. Extracted variables included study design, population characteristics, intervention details, comparator, sample size, and glycemic outcomes. Discrepancies were resolved by consensus or a third reviewer.

Sex differences were assessed across four prespecified domains. (1) Blood glucose: we analyzed TIR, attainment of fasting blood glucose (FBG) targets, and attainment of postprandial glucose (PPG) target. (2) HbA1c: outcomes included achievement of HbA1c target, change in HbA1c from baseline to follow-up, and mean HbA1c at the end point. (3) Hypoglycemia: we extracted incidence (≥1 episode), event frequency (episodes per person or per unit time, as reported). (4) Insulin dosage: we extracted total daily dose (TDD) and weight-normalized dose (units/kg). The definitions of outcomes are summarized in . Definitions or units varied across studies; we used the original authors’ definitions and converted units when feasible; men were the reference group for effect estimation.

Risk of bias was assessed according to study design. RCTs were evaluated using the Cochrane RoB 2 tool, and observational studies with the Newcastle-Ottawa Scale (NOS). NOS scores were converted to categories of low risk, some concerns, or high risk for comparability with RoB 2. Visualizations were generated with the robvis tool.

We applied the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework to rate the certainty of evidence for each outcome. Evidence was graded as high, moderate, low, or very low, based on risk of bias, inconsistency, indirectness, imprecision, and publication bias. Summary of findings tables were created to present pooled estimates alongside GRADE ratings.

Sensitivity analyses were conducted using a leave-one-out approach, sequentially removing one study at a time from the meta-analysis to test robustness. Prespecified subgroup analyses were performed by diabetes type (type 1 diabetes vs type 2 diabetes), given their distinct pathophysiology and treatment responses.

For each outcome, studies were included in the meta-analysis only if they reported sex-specific data in the required metric—for dichotomous outcomes, events and totals for women and men (RR); for continuous outcomes, means and SDs (or convertible SEs) with sample sizes for each sex at the prespecified end point (SMD). Studies reporting incompatible metrics or lacking raw counts/SDs were excluded from pooling and summarized narratively.

Using the search strategies described above, we identified 3084 papers from Scopus, 110 from the Cochrane, 200 from Google Scholar database, and 65 from PubMed, bringing the total number of papers screened to 3459. Among these papers, 65 papers were duplicated. Screening the title of each paper identified 211 potentially relevant citations for abstract review. After reviewing abstracts, 65 relevant papers were selected for full-text review. We applied the inclusion and exclusion criteria based on the full text of the paper. Finally, a total of 24 key papers, were identified to be included, comprising RCTs (n=8) and observational studies (n=16) (). lists the included studies and their major findings. All studies reported at least one sex-related difference in glycemic outcomes or insulin use. Study and population characteristics are summarized in . Risk-of-bias assessment is shown in . For RCTs evaluated with the RoB 2 tool, most domains were judged as low risk, particularly in randomization, outcome measurement, and reporting. Some concerns were noted in a few studies regarding missing data, deviations from interventions, and post hoc subgroup analyses, but no RCT was rated at overall high risk of bias.

In contrast, observational studies appraised using the NOS showed greater variability. Selection bias was frequent due to single-center recruitment or convenience sampling, and comparability was often limited by insufficient adjustment for confounders. Several observational studies were judged at overall high risk of bias, particularly where outcome measurement or control for covariates was weak.

Leave-one-out analyses () suggested that generally, the omission of individual studies did not materially alter the direction or statistical significance of the primary outcomes, particularly for type 1 diabetes parameters and outpatient hypoglycemia incidence. However, single studies were identified as significant drivers of between-study variance in type 2 diabetes outcomes. In the analysis of HbA1c target achievement for type 2 diabetes, the exclusion of Tran et al eliminated statistical heterogeneity (I 2 reduced from 80.3% to 0%) and shifted the pooled result to statistical significance (RR 0.80, 95% CI 0.74 to 0.86, p<0.0001). Similarly, for insulin dose in type 2 diabetes, omitting Kautzky-Willer et al resolved heterogeneity (I 2 =0%) and strengthened the effect size (SMD 0.74, 95% CI 0.45 to 1.02), although the finding remained statistically significant. In the case of inpatient hypoglycemia, Li et al was the primary source of heterogeneity; its removal reduced I 2 to 0% but did not alter the non-significant overall association.

Four studies assessed TIR, FBG, or PPG, but their findings were inconsistent. Regarding type 1 diabetes, Ying et al reported a higher TIR in women (TIR 61.0±26.2% vs 44.1±18.1%, p=0.06; SMD 0.78; 95% CI −0.01 to 1.57, p=0.05). The certainty of this evidence was rated as moderate and downgraded due to reliance on a single study. Conversely, in the context of type 2 diabetes, Liu et al observed an opposite trend (SMD −0.33; 95% CI −0.73 to 0.06; p=0.56). The certainty of evidence for this outcome was low, primarily due to the inclusion of non-randomized study designs and the limited number of studies. Among individuals with type 2 diabetes, women were also less likely to achieve PPG targets compared with men (RR 0.88; 95% CI 0.68 to 1.15; p=0.31). The certainty of evidence was very low due to the wide CI and imprecision. Pooled analyses were conducted only when more than one study was available for a given outcome.

Nine studies reported sex differences in HbA1c. We excluded Esteves et al from the pooled estimation, as it was the sole study reporting HbA1c reduction rather than absolute values. That study found that women with type 1 diabetes experienced a significantly greater reduction in HbA1c compared with men (median (quartile) −1.100 (−2.200 to −0.400) vs −0.100 (−0.800 to 0.400); p=0.002). In type 1 diabetes, the pooled analysis showed no significant difference in women achieving an HbA1c target of <7.0% compared with men (RR 1.05, 95% CI 0.91 to 1.22, p=0.09). The certainty of this evidence was rated as very low, primarily due to the reliance on self-reported data and lack of adjustment for confounding variables in several included studies. In type 2 diabetes, the pooled results suggested that men more frequently achieved HbA1c <7% and had lower mean HbA1c at follow-up (RR 0.86, 95% CI 0.72 to 1.03, p=0.01). This evidence was graded as moderate certainty, downgraded from high due to substantial statistical heterogeneity (I² >70%). Because of this, we also implemented narrative synthesis regarding HbA1c target achievement for type 2 diabetes. Kautzky-Willer et al and Gourdy et al both reported significantly higher achievement rates in men compared with women (33.0% vs 26.5% and 27.2% vs 21.6%, respectively) following insulin initiation. Similarly, Dwiyatna et al observed a trend in Indonesian outpatients where women were less likely to reach the target (OR 0.396, p=0.055). In contrast, Tran et al , in a large population-based study of Norwegian general practice, found that Western women achieved the target more frequently than men (66.1% vs 62.3%), a pattern that persisted across several ethnic minority groups.

Eight studies evaluated hypoglycemia incidence. Mellor et al and Shahid et al were excluded from the meta-analysis because they did not report absolute event counts, rendering them incompatible with the calculation of relative risks. Cardona et al was also excluded from pooled estimation because the study population consisted exclusively of individuals who experienced hypoglycemia, precluding comparison with a non-event group. In addition, He et al was excluded because hypoglycemia was defined by symptoms or self-monitored blood glucose readings rather than by standardized glycemic thresholds. Because most included studies did not stratify results by diabetes type, analyses were pooled across individuals with type 1 and type 2 diabetes. However, given the distinct determinants of hypoglycemia across care environments, studies were stratified by clinical setting (inpatient vs outpatient). In the inpatient setting, the random-effects model suggests that men have a slightly higher risk of hypoglycemia (RR 0.78, 95% CI 0.33 to 1.83, p=0.01), the certainty of evidence is low due to a failure to control for confounding by indication.

Conversely, for the outpatient setting, the pooled results indicated a marginally higher risk in women (RR 1.08, 95% CI 0.61 to 1.89, p=0.035). The certainty for this finding was also low, attributed to heterogeneous interventions across studies and the inclusion of highly specific study populations.

Given the substantial heterogeneity, narrative synthesis was undertaken to contextualize these findings. In inpatient cohorts, Cardona et al managed studies in Spain. They took data from individuals with type 2 diabetes who took in-hospital insulin therapy (basal-bolus, basal+sliding scale insulin, or insulin with oral antidiabetic drug). They reported that asymptomatic hypoglycemia was more common in men (OR 2.08, 95% CI 1.13 to 3.83, p=0.02, females reference), and Li et al implemented an RCT in China, by randomized individuals with type 2 diabetes to NovoMix 30 and Humalog Mix 50 crossover. They similarly observed a higher incidence of hypoglycemia among males (p<0.001). In contrast, Akirov et al , who involved individuals with both type 1 and type 2 diabetes in Israel to their study, used long-acting basal insulin (insulin glargine or insulin detemir) and pre-mixed insulin preparations as their treatment arm and identified female sex as a risk factor for hypoglycemia (OR 1.31, 95% CI 1.10 to 1.60; male reference), highlighting inconsistent directional effects within inpatient settings.

In outpatient cohorts, Mellor et al analyzed multicenter data including individuals with both type 1 and type 2 diabetes treated with a range of insulin regimens, including basal, basal-bolus, and premixed insulin, using both human and analog formulations. They reported that hypoglycemia was more strongly associated with female sex (RR 0.814, 95% CI 0.779 to 0.850; female reference), while Kautzky-Willer et al analyzed multicenter data from Europe, Asia, and the Americas in individuals with type 2 diabetes treated with insulin glargine or neutral protamine Hagedorn (NPH) insulin in addition to oral antidiabetic drugs over 24–36 weeks and found a significantly higher proportion of women experiencing hypoglycemic episodes overall (p=0.02). He et al conducted a study in China involving individuals with either type 1 or type 2 diabetes who had been treated with insulin for >12 months; the specific insulin regimens were not reported. Their findings demonstrated an increased risk of severe hypoglycemia in females with both type 1 diabetes (RR 1.12, 95% CI 0.97 to 1.29) and type 2 diabetes (RR 1.22, 95% CI 1.08 to 1.97; male reference). Conversely, Brockman et al examined individuals with type 1 diabetes in Canada treated with CSII or MDI and observed that, within 6 hours following exercise, males experienced significantly more hypoglycemia than females, as measured by the area under the curve for time spent below glycemic thresholds (21.6±26.0 vs 3.8±8.0; p=0.042). Finally, Shah et al conducted a study in the USA among individuals with type 1 diabetes using insulin pumps or MDI and reported no significant sex differences in severe hypoglycemia among individuals with type 1 diabetes (p=0.42).

Five studies examined insulin requirements. In type 1 diabetes, men exhibited a trend toward higher TDD per kg, although the pooled estimate was not statistically significant (SMD –0.35, 95% CI –0.81 to 0.11, p=0.62). The certainty of this evidence was rated as very low, downgraded due to serious imprecision (wide CI) and the observational nature of the original studies. In type 2 diabetes, women consistently required higher per-kg doses than men (SMD 0.55, 95% CI 0.23 to 0.86, p=0.007). This evidence was graded as very low certainty, primarily due to a failure to control for confounding by indication. and present the individual results of each study included in this review, as well as the pooled analysis of these results. The code used to synthesize these results is provided in . GRADE certainty of the evidence ranged from high to very low ().

This systematic review identified consistent evidence of sex differences across various glycemic outcomes and insulin usage in individuals with diabetes, although the certainty of this evidence ranges from moderate to very low. Our exploratory synthesis suggests that women with type 2 diabetes demonstrated a reduced likelihood of achieving favorable treatment effects on HbA1c, exhibited lower TIR, and required higher insulin dosages per kg compared with men. This observation aligns with findings from Esteghamati et al . Conversely, individuals with type 1 diabetes exhibited opposing patterns regarding TIR and insulin dosages per kg, although no significant differences were observed in HbA1c treatment effects.

Unlike type 1 diabetes, the contemporary management of type 2 diabetes frequently necessitates polypharmacy, including agents such as metformin, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter 2 inhibitors. Consequently, findings regarding insulin dosage in type 2 diabetes must be contextualized within a framework where insulin therapy is initiated against a backdrop of significant insulin resistance and concurrent medication use. The higher relative doses observed in women may reflect complex interactions between sex-specific adiposity distribution and hormonal variations; however, these observations may also be influenced by the confounding effects of polypharmacy.

From a clinical interpretation standpoint, the observed sex-linked patterns should be regarded as hypothesis-generating. For people with type 2 diabetes, the observed trends toward lower HbA1c target attainment (low certainty) and higher per-kilogram insulin dose requirements (very low certainty) raise the hypothesis that standard weight-based titration strategies may be less effective for some women, particularly those with higher visceral adiposity. Rather than advocating deviations from established dosing algorithms, these findings support increased clinical vigilance during insulin titration in women, with closer monitoring of response trajectories to balance potential insulin resistance against the risk of overtreatment. For people with type 1 diabetes, the moderate certainty evidence for sex differences in TIR suggests that disparities in glycemic variability are relatively robust in this population. This finding supports the hypothesis that women may benefit more distinctly from technology-supported delivery (eg, closed-loop systems) to counter cyclical hormonal variations, a concept that warrants targeted investigation in future trials.

Regarding hypoglycemia, our meta-analysis and review of the literature consistently identified sex as a variable that needs to be considered, with numerous studies reporting distinct risk profiles between males and females. However, the direction of this risk varies substantially across studies, a heterogeneity likely attributable to the specific environmental drivers in different care settings. In inpatient cohorts, the observed sex differences are often confounded by the severity of concurrent illness, as factors related to hypoglycemia include advanced age, comorbidities including heart and liver failure, sepsis, and malnutrition, among others. Conversely, in outpatient settings, the variability is largely driven by ‘daily lifestyle’ factors—such as differences in physical activity intensity and dietary adherence—which frequently differ by sex but are rarely fully adjusted for in observational models. These findings suggest that biological sex may function as a contextual risk marker rather than a standalone predictor of hypoglycemia. An integrated risk stratification framework that evaluates sex in combination with setting-specific factors may therefore provide a more precise approach to individualized prevention.

Translationally, these findings support incorporating sex-specific considerations into diabetes management; however, given the substantial heterogeneity across studies, such considerations are best applied within a personalized management strategy rather than as uniform, sex-based algorithms. This review also highlights several research and reporting priorities. Future trials and real-world studies should prespecify sex-stratified endpoints, consistently report sex-disaggregated data, and explicitly control for confounders relevant to insulin dose requirements and hypoglycemia risk. Standardized definitions of hypoglycemia and harmonized continuous glucose monitor (CGM) reporting would improve comparability and facilitate higher-certainty evidence synthesis.