Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer

Domain: chemistry
Published: 2026-02-16
Editorial outcome: major_revision

Abstract
Insulin resistance is suggested to be a risk factor for cancer; however, large-scale epidemiological evidence linking insulin resistance to cancer remains limited. Here we apply a machine learning-based prediction model of insulin resistance with nine clinical parameters, termed artificial intelligence–derived insulin resistance (AI-IR), to the UK Biobank and demonstrated that AI-IR exhibits the highest predictive performance for diabetes incidence compared to body mass index (BMI), metabolic syndrome (MetS), triglyceride to high-density lipoprotein cholesterol (TG/HDL) ratio, and triglyceride-glucose (TyG) index. Moreover, AI-IR is significantly associated with an increased risk of six cancers (uterine, kidney, esophagus, pancreas, colon, and breast) and showed nominal associations with six additional cancers (renal pelvis, small intestine, stomach, liver and gallbladder, leukemia, and bronchial and lung). When we define composite cancers by merging cancer types whose risks increase with AI-IR, age- and sex-adjusted hazard ratio is 1.25 (95% confidence interval, 1.20-1.31; P < 1 × 10-11). AI-IR is a better predictor of the composite cancers compared to BMI and TyG index, while its capability is comparable to that of MetS and TG/HDL ratio. We conclude that AI-IR is a robust metric for predicting both diabetes and the composite cancer incidence and could be utilized for identification of high-risk individuals and focused screening.

Introduction
Insulin resistance, defined as an impaired response of tissues such as liver, muscle, and adipose tissue to insulin1, is one of the fundamental etiologies of diabetes along with impaired insulin secretion. Indeed, insulin resistance precedes the development of type 2 diabetes1 and also predisposes people to cardiovascular disease2. Insulin resistance is often caused by obesity and its associated chronic inflammation, and is accompanied by compensatory hyperinsulinemia.

Accumulating evidence indicates that both diabetes3–5 and obesity6–8 are associated with a higher risk of cancer. Therefore, it is reasonable to speculate that insulin resistance, which links diabetes and obesity, is also a risk factor for cancer. In this regard, it has been proposed that insulin resistance and hyperinsulinemia might contribute to cancer growth through the anabolic action of insulin and also because of cross-reactivity of insulin with insulin-like growth factor 1 receptor9. The mechanistic link between inflammation and cancer has also been actively investigated10. Similarly, metabolic syndrome, which is linked to visceral adiposity and its associated insulin resistance11, is also associated with a higher risk of cancer12–17. However, large-scale epidemiological evidence directly linking insulin resistance and cancer is limited, with only a few studies examining the incidence of endometrial and colorectal cancer using high fasting insulin as a surrogate marker for insulin resistance18,19, because insulin resistance is rarely evaluated in patients, except for those being treated in specialized diabetes clinics. The gold standard method for assessing insulin resistance, the hyperinsulinemic-euglycemic clamp, is not feasible in regular clinical settings. The homeostasis model assessment of insulin resistance (HOMA-IR) index, in which the product of fasting plasma glucose (FPG) and fasting plasma insulin is divided by a constant20, has been widely used as an alternative method because HOMA-IR involves the direct measurement of insulin and glucose, and has been shown to correlate strongly with the gold standard clamp technique21,22. Nevertheless, fasting plasma insulin is also not routinely evaluated in general practice. Therefore, the possible effect of insulin resistance on cancer incidence has been difficult to examine at the population scale. Recently, we developed a machine learning-based prediction model of insulin resistance (i.e., HOMA-IR > 2.5) that considers nine clinical parameters (age, sex, race, body mass index [BMI], FPG, glycohemoglobin, triglyceride [TG], total cholesterol, and high-density lipoprotein cholesterol [HDL]) in a non-diabetic population of both European and Asian ancestries, using the US National Health and Nutrition Examination Survey (NHANES) cohort and the Taiwan MJ cohort. The area under the curve of our prediction model was as high as 0.8823.

Here, we apply the machine learning-based prediction model, termed artificial intelligence–derived insulin resistance (AI-IR), to the UK Biobank cohort to examine the effect of predicted insulin resistance on the incidence of diabetes, cardiovascular disease, and cancer at the population scale. AI-IR outperforms previously established metrics in predicting diabetes and is also significantly associated with specific types of cancer.

Results
Machine learning-predicted insulin resistance was associated with a higher risk of diabetes, cardiovascular disease, and mortality in the UK Biobank
To evaluate the predictive capability of AI-IR (Fig. 1a) for the incidence of diabetes, cardiovascular disease, and mortality in the UK Biobank, we first looked at the effect of AI-IR on the incidence of diabetes among participants without diabetes at baseline and completed follow-up visit (N = 14,165, baseline characteristics are shown in Supplementary Table 1). During a mean follow-up of 4.28 years (95% confidence interval [CI], 3.41–5.15), 309 participants (2.18%) developed diabetes. AI-IR positive participants had a significantly higher risk of developing diabetes compared to AI-IR negative participants (odds ratio [OR], 7.31; 95% CI, 5.75–9.30; P < 1 × 10-10, Fig. 1b). Notably, although AI-IR was originally developed using fasting blood test results, blood samples in the UK Biobank were not consistently collected in the fasted state. To address this, we stratified participants into three groups based on fasting duration prior to blood collection (less than 4 h, 4 or more and less than 8 h, 8 h or more) and examined the association between AI-IR and the incidence of diabetes. Across all categories, AI-IR positive participants consistently showed a significantly higher risk of developing diabetes, supporting the robustness of our model (Supplementary Fig. 1a–c). Hereafter, to maintain maximum sample size, we did not stratify or exclude participants based on fasting duration. In a complementary analysis leveraging the National Health Service (NHS) medical record system, we looked at the effect of AI-IR on hospital admission with diabetes among participants without diabetes at baseline (Baseline characteristics of participants are shown in Supplementary Table 2). AI-IR positive participants had a markedly higher risk of admission with diabetes (hazard ratio [HR], 6.44; 95% CI, 6.19–6.69; P < 1 × 10−10, Fig. 1c). Of note, AI-IR was associated with a significantly higher risk of diabetes onset and hospital admission with diabetes after adjusting for age and sex, and even after adjusting for age, sex, and BMI, indicating that our machine learning-based prediction model is able to capture a BMI-independent effect of insulin resistance (Supplementary Fig. 2a, b).

Cardiovascular disease is a major complication of insulin resistance and diabetes. Therefore, we also looked at the effect of AI-IR on 3- and 4-point major adverse cardiovascular event (MACE). Effects on cardiovascular mortality and overall mortality were also examined. Baseline characteristics of participants are shown in Supplementary Table 2. As expected, a Kaplan-Meier plot revealed a significant difference in the cumulative incidence of 3-point MACE between participants with diabetes, those without diabetes but positive for AI-IR, and those without diabetes and negative for AI-IR (Fig. 1d, log-rank test P < 1 × 10−10). The HR adjusted for age and sex was highest in participants with diabetes (HR, 1.84; 95% CI, 1.76-1.92; P < 1 × 10−10), followed by those without diabetes but positive for AI-IR (HR, 1.38; 95% CI, 1.34–1.42; P < 1 × 10−10), and then those without diabetes and negative for AI-IR (Fig. 1d). AI-IR and diabetes was significantly associated with a higher risk of 3-point MACE even after adjusting for age, sex, and BMI (Supplementary Fig. 2c). We also observed that AI-IR was significantly associated with an increased risk of 4-point MACE, cardiovascular mortality, and overall mortality (Fig. 1e and Supplementary Fig. 2d–f).

Machine learning-predicted insulin resistance enables improved diabetes risk stratification compared to previously reported metrics
To examine whether AI-IR outperforms previously established simpler metrics of obesity, metabolic syndrome, and insulin resistance, we compared the predictive capabilities of AI-IR, BMI, metabolic syndrome (MetS), TG/HDL ratio, and TyG index for the incidence of diabetes among individuals without diabetes at baseline during the follow-up period. TG/HDL ratio24,25 and TyG index26–28 has been reported as surrogate markers of insulin resistance. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was highest for AI-IR (0.798, P < 1 × 10−4 vs other metrics), followed by MetS (0.748), BMI (0.721), TyG index (0.703), and TG/HDL ratio (0.702), indicating that the AI-IR demonstrated the highest predictive performance among these metrics (Fig. 2a). Next, we categorized individuals without diabetes at baseline into four groups according to both AI-IR status (positive or negative) and their status on previously established metrics (positive or negative). BMI of 30, TG/HDL ratio of 3.025, and TyG index of 4.6827 were used as cut-off values according to previous literatures. When we categorized individuals into four groups based on AI-IR status (positive or negative) and BMI (≥30 or <30), we observed that the incidence of diabetes was significantly higher (P = 2.52 × 10−5) in the AI-IR single-positive group (OR, 6.14; 95% CI, 4.42–8.54; P < 1 × 10−20; adjusted for age and sex) compared with the BMI single-positive group (OR, 0.86; 95% CI, 0.35–2.12; P = 0.74). Of note, a BMI of 30 or higher alone did not significantly increase diabetes incidence when individuals were negative for AI-IR, suggesting that these subjects may represent a metabolically healthy obesity phenotype. In contrast, diabetes incidence was markedly higher (P = 9.78 × 10−7) in the AI-IR and BMI double-positive group (OR, 8.08; 95% CI 6.13–10.66; P < 1 × 10−20) compared with the BMI single positive group, underscoring that AI-IR provides substantially improved diabetes risk stratification among individuals with obesity (Fig. 2b). Similarly, when we categorized individuals without diabetes into four groups based on both AI-IR status (positive or negative) and MetS (positive or negative), the incidence of diabetes was significantly higher (P = 1.55 × 10−3) in the AI-IR single-positive group (OR, 6.85; 95% CI, 4.35–10.80; P = 1.2 × 10−16) compared with the MetS single-positive group (OR, 3.14; 95% CI, 2.07–4.77; P = 6.8 × 10−8). Again, the incidence of diabetes was even greater (P = 2.19 × 10−13) in the AI-IR and MetS double-positive group (OR, 11.71; 95% CI 8.53–16.06; P < 1 × 10−20) compared with the MetS single-positive group (Fig. 2c). We observed the same pattern for TG/HDL ratio and TyG index (Fig. 2d, e). Collectively, these results indicated that AI-IR was significantly associated with a higher risk of diabetes, cardiovascular disease, and mortality in the UK Biobank. Moreover, AI-IR demonstrated the highest predictive capability for the incidence of diabetes compared to previously established metrics, providing the rationale for investigating the association between predicted insulin resistance and cancer incidence.

Effect of machine learning-predicted insulin resistance on the incidence of cancer
To investigate the effect of AI-IR on the incidence of cancer, we leveraged the linkage between the UK Biobank and the NHS medical record system and examined the incidence of cancer among participants who were cancer-free at the baseline visit (N = 372,395). We compared participants without diabetes and negative for AI-IR (N = 256,685), those without diabetes but positive for AI-IR (N = 94,782), and those with diabetes (N = 20,928). Of 372,395 participants who were cancer-free at baseline, 51,193 developed cancer (Supplementary Table 3). When we merged all types of cancer, we did not observe any differences in cancer incidence between the three groups. The HR adjusted for age and sex in participants without diabetes but positive for AI-IR was 1.012 (95% CI, 0.992–1.033; P = 0.228), and that in participants with diabetes was 0.973 (95% CI, 0.938–1.008; P = 0.129) (Fig. 3 and Supplementary Table 4). However, when we looked at individual types of cancer, both AI-IR and diabetes were associated with a significantly higher risk of incidence of multiple cancer types (The results of the 25 common cancers are shown in Fig. 3, and the complete list can be found in Supplementary Table 4). We examined the incidence of 36 cancers common to both males and females, 4 cancer types specific to males, and 3 cancer types specific to females. The Bonferroni-corrected P value for significance was 0.05/43 = 1.163 × 10−3. The effect of AI-IR on increasing the risk of the incidence was strongest for uterine cancer (HR; 2.340; 95% CI, 2.065-2.652; P = 1.00 × 10−9), followed by kidney cancer (HR, 1.557; 95% CI, 1.367–1.772; P = 1.00 × 10−9) and esophagus cancer (HR, 1.464; 95% CI, 1.253–1.710; P = 1.61 × 10−6). It was also associated with a higher incidence of renal pelvis cancer (HR, 1.417; 95% CI, 1.013–1.983; P = 0.0418), small intestine cancer (HR, 1.393; 95% CI, 1.019–1.905; P = 0.0376), stomach cancer (HR, 1.374; 95% CI, 1.132–1.667; P = 1.28 × 10−3), liver and gallbladder (GB) cancer (HR, 1.367; 95% CI, 1.114–1.678; P = 2.73 × 10−3), pancreas cancer (HR, 1.291; 95% CI, 1.117–1.492; P = 5.58 × 10−4), colon cancer (HR, 1.176; 95% CI, 1.084–1.276; P = 9.45 × 10−5), leukemia (HR, 1.164; 95% CI, 1.012–1.339; P = 0.0337), bronchial and lung cancer (HR, 1.136; 95% CI, 1.048–1.231; P = 2.02 × 10−3), and breast cancer (HR, 1.135; 95% CI, 1.075–1.199; P = 5.92 × 10−6). When we considered the Bonferroni correction, AI-IR was associated with a higher incidence of uterine, kidney, esophagus, pancreas, colon, and breast cancers. On the other hand, AI-IR was associated with a significantly lower incidence of skin cancer (HR, 0.852; 95% CI, 0.825–0.881; P = 1.00 × 10−9). For cancer types whose incidences were increased or decreased by AI-IR, diabetes also exhibited an effect in the same direction, whereby in many cases, the effect size was numerically larger (Fig. 3 and Supplementary Table 4).

When we defined composite cancers by merging 10 cancer types (common to both males and females) whose risks were either significantly increased (kidney, esophagus, pancreas, colon) or nominally increased (renal pelvis, small intestine, stomach, liver and GB, colon, and bronchial and lung) by AI-IR, a Kaplan–Meier plot revealed a significant difference between participants with diabetes, those without diabetes but positive for AI-IR, and those without diabetes and negative for AI-IR, on the cumulative incidence of the composite cancers (Fig. 4a). The HR adjusted for age and sex in participants without diabetes but positive for AI-IR was 1.25 (95% CI, 1.20–1.31; P < 1 × 10−11), and that in participants with diabetes was 1.40 (95% CI, 1.31–1.50; P < 1 × 10−11). When we adjusted also for BMI, the HR in participants without diabetes but positive for AI-IR was 1.16 (95% CI, 1.10–1.22; P = 1.2 × 10−8), suggesting that approximately 36% of AI-IR’ effect is mediated through BMI. Sex-specific analysis revealed that the impact of AI-IR on the incidence of the composite cancers remained consistent in both males and females (Supplementary Fig. 3a, b). When we combined female-specific uterine and breast cancers whose risks were significantly increased by AI-IR, we observed that effect the HR adjusted for age in participants without diabetes but positive for AI-IR was 1.26 (95% CI, 1.20–1.32; P < 1 × 10−11), which was comparable to that in participants with diabetes (HR, 1.23; 95% CI, 1.11–1.36; P < 3.9 × 10−5) (Fig. 4b). A Kaplan–Meier plot also revealed a significant difference between three groups on the cumulative incidence of specific cancers such as uterine cancer (Supplementary Fig. 4a), kidney cancer, colon cancer, and bronchial and lung cancer (Supplementary Fig. 4a–d).

Cancer risk is well known to increase with age29,30. Indeed, when we stratified participants by age at enrollment (40–69 years old), the incidence of the composite cancers per 1000 person-years increased with age (Supplementary Fig. 5a). Furthermore, both AI-IR and diabetes were associated with a higher incidence of the composite cancers across all ages (Supplementary Fig. 5a), indicating that AI-IR is the risk factor for the composite cancers across different age groups. Kaplan–Meier analyses further confirmed consistent differences in cumulative incidence of the composite cancers among three groups across different ages at enrollment (<50 years old, 50–59 years old, and ≥60 years) (Supplementary Fig. 5b–d). To more rigorously account for confounding by age, we also conducted analyses with age as the underlying time variable. Significant differences in cancer incidence persisted among participants with diabetes, those without diabetes but AI-IR positive, and those without diabetes and AI-IR negative (Supplementary Fig. 6a.) The HR adjusted for sex and BMI in participants without diabetes but AI-IR positive was 1.16 (95% CI, 1.10–1.22; P < 3.2 × 10−8), and that in participants with diabetes was 1.29 (95% CI, 1.20–1.39; P < 1 × 10−10). The effect of AI-IR on 3-point MACE was also consistent in these models (Supplementary Fig. 6b). Together, these findings demonstrate the robustness of AI-IR in predicting complications of insulin resistance while fully accounting for the effect of age.

BMI-dependent and -independent effect of machine learning-predicted insulin resistance on cancer incidence
In our prediction model, feature importance was highest for higher BMI (0.427), followed by higher FPG (0.115), lower HDL (0.115), and higher TG (0.097)23. To explore the BMI-independent effect of AI-IR on cancer incidence, we also performed an analysis adjusted for age, sex, and BMI (Fig. 4c and Supplementary Table 5). Among the cancers whose risks were positively associated with AI-IR, we observed that effects for renal pelvis, small intestine, stomach, liver and GB, pancreas, colon, leukemia, and breast cancer were BMI-dependent (Fig. 4c). However, we observed that the effect of AI-IR on the incidence of bronchial and lung cancer became stronger and significant even after Bonferroni correction when we adjusted for BMI in addition to age and sex (HR, 1.33; 95% CI, 1.20–1.47; P = 1.71 × 10−8), than when we adjusted only for age and sex (HR, 1.14; 95% CI, 1.05–1.23; P = 2.02 × 10−3) (Fig. 4c), indicating that the effect was independent of BMI. The effect on uterine, kidney, and esophagus cancer remained nominally significant. Additionally, the association between AI-IR and lower risk of skin cancer was also BMI-independent.

Lung cancer is strongly associated with smoking, the leading risk factor for the global burden of cancer31. We observed that the effect of AI-IR on the incidence of bronchial and lung cancer is significant even when we adjusted also for smoking status (never smoker, previous smoker, and current smoker); The same is true for the incidence of the composite cancers (Supplementary Fig. 5a, b). We also looked at a possible joint effect of AI-IR and smoking status on the incidence of bronchial and lung cancer (Supplementary Fig. 5c). In the never smoker group, HRs adjusted for age, sex, and BMI were not significantly different (P = 0.513) between the AI-IR positive group (HR, 1.08; 95% CI, 0.85–1.37) and the AI-IR negative group. In the current smoker group, HRs adjusted for the three factors were also not significantly different (P = 0.726) between the AI-IR positive group (HR, 17.42; 95% CI, 14.66–20.71) and the AI-IR negative group (HR, 17.89; 95% CI, 15.63–20.47). On the other hand, in the previous smoker group, HRs adjusted for the three factors in the AI-IR positive group (HR, 5.52; 95% CI, 4.69–6.49) was significantly higher than that in the AI-IR negative group (HR, 3.84; 95% CI, 3.35–4.40), when examined by post-hoc analysis (P = 7.78 × 10−8, Supplementary Fig. 5c). We observed similar interaction effect on the incidence of the composite cancers; predicted insulin resistance increased the incidence in the never smoker group and the previous smoker group, but not in the current smoker group (Supplementary Fig. 5d). Notably, the composite cancers include esophagus, pancreas, stomach, liver, and bronchial and lung cancers, all of which are known to have increased risk due to smoking32. These results suggest that the effect of AI-IR on the incidence of bronchial and lung cancer or the composite cancers is the most tangible in the previous smoker group; however, the effect may have been masked in the current smoker groups because the absolute risks of the incidence are overwhelmingly high.

Machine learning-predicted insulin resistance enables improved cancer risk stratification compared to previously reported metrics
Finally, to examine whether AI-IR provides a better prediction of the composite cancer incidence than previously established metrics such as BMI, MetS, TG/HDL ratio, and TyG index, we classified participants who were free of diabetes and cancer at baseline into four groups according to their AI-IR status (positive or negative) and the status of these metrics (positive or negative). When we consider AI-IR and BMI, we observed that the incidence of the composite cancers was significantly higher (P = 2.28 × 10−2) for the AI-IR single-positive group (HR, 1.18; 95% CI, 1.11–1.26; P < 9.5 × 10−8) compared with the BMI single-positive group (HR, 1.04; 95% CI, 0.95–1.15; P = 0.38). BMI of 30 or higher alone did not significantly increase the composite cancer incidence when individuals were negative for AI-IR. We also observed that the incidence of the composite cancers was significantly higher (P = 2.01 × 10−5) in the AI-IR and BMI double-positive group (HR, 1.30; 95% CI 1.24–1.37; P < 1 × 10−11) compared with the BMI single positive group, underscoring that AI-IR provides substantially improved cancer risk stratification among individuals with obesity (Fig. 5a). When considering AI-IR (positive or negative) and MetS (positive or negative), the AI-IR single positive group (HR, 1.21; 95% CI 1.12–1.31; P = 2.1 × 10−6) and the MetS single positive group (HR, 1.19; 95% CI 1.12–1.26; P = 1.8 × 10−9) exhibited comparable HRs (P = 0.649) for the incidence of the composite cancers. However, the incidence of composite cancers was significantly higher (P = 1.49 × 10−4) in the AI-IR and MetS double-positive group (HR, 1.34; 95% CI 1.27–1.40; P < 1 × 10−11) compared with the MetS single-positive group (Fig. 5b). Similar patterns were observed when we consider AI-IR and TG/HDL ratio (Fig. 5c). When considering AI-IR and TyG index, the incidence of the composite cancers was significantly higher (P = 1.92 × 10−2) for the AI-IR single-positive group (HR, 1.23; 95% CI, 1.13–1.35; P = 7.2 × 10−6) compared with the TyG single-positive group (HR, 1.10; 95% CI, 1.05–1.16; P = 1.1 × 10−4). We also observed that the incidence of the composite cancers was significantly higher (P < 1 × 10−11) for the AI-IR and TyG double-positive group (HR, 1.32; 95% CI 1.26–1.39; P < 1 × 10−11) compared to the TyG single-positive group (Fig. 5d). Altogether, these results indicate that AI-IR enables improved cancer risk stratification compared to previously established metrics.

Methods
The UK Biobank
The present study has been approved by the UK Biobank (approved research ID 78912), which in turn was approved by the National Health Service (NHS) North West Multi-centre Research Ethics Committee. The UK Biobank is a prospective cohort of individuals aged 40–69 years at recruitment between 2006 and 201045. All participants provided written informed consent. At baseline, participants completed lifestyle and health-related questionnaires, interviews, and physical measurements. Genotype and biomarker data were also collected, and health-related outcomes were obtained via linkage to NHS medical records.

Machine learning-based prediction model of insulin resistance (AI-IR)
Previously, we developed and validated a machine learning-based prediction model of insulin resistance23. Briefly, an XGboost model was trained to predict the probability that HOMA-IR exceeds 2.5 in non-diabetic populations, by considering nine clinical parameters (age, sex, race, BMI, FPG, glycohemoglobin, TG, total cholesterol, and HDL cholesterol). Participants were classified as AI-IR positive if the predicted probability was greater than 0.5. The model achieved an area under the curve of 0.88.

Ascertainment of disease prevalence and incidence
The primary outcomes of the current work were the incidence of overall and site-specific cancers in participants without a history of cancer at enrollment. Cancer incidence was defined as the first diagnosis after enrollment, with cancer type (ICD-10) and date obtained from UK Biobank Data-Fields 40005 and 40006. Follow-up was conducted until the last available date in the registries: 31 December 2020 for England, 30 November 2021 for Scotland, and 31 December 2016 for Wales. Participants without cancer were censored at the last available date of the respective registry, ensuring consistent follow-up for all participants. For participants recorded in the combined “Originating from England/Wales (E/W)” registry, it was not possible to assign registry-specific censor dates to each participant. In these cases, censor dates were assigned as 31 December 2016 for participants born in Wales (determined by Data-Fields 1647 and 20115), and 31 December 2020 for all others.

AI-IR status was defined using nine parameters obtained at enrollment. Race, as implemented in the AI-IR model, was derived from self-reported race (UK Biobank Data-Field 21000) collected at baseline. This variable was included as a demographic input to account for population-level differences in clinical measurements and healthcare context, and was not intended to represent genetic ancestry or biological determinants. Diabetes status was defined using observed phenotypes. Participants with HbA1c > 6.5% at enrollment and/or self-report of a physician’s diagnosis were classified as having diabetes. For short-term follow-up analysis in the UK Biobank, new-onset of diabetes was defined using follow-up HbA1c and/or self-report of physician’s diagnosis, while long-term follow-up relied on NHS-recorded diagnosis (ICD-10 E08, 09, 11, and 13), with diagnosis date defined as the date of admission with diabetes. All participants with diabetes were included in these analyses.

Incidences of 3-point major adverse cardiovascular event (MACE; a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke) and 4-point MACE (3-point MACE plus heart failure requiring hospitalization), overall mortality, and cardiovascular mortality were also evaluated. We used algorithmically-defined outcomes (Category 42), which were derived from baseline assessment, hospital admissions, and death registries. Non-fatal myocardial infarction and non-fatal stroke were defined using Data-Fields 42000 and 42006, respectively. Heart failure requiring hospitalization was obtained from hospital episode statistics (Resource 138483; ICD-10 I50.9), and mortality data were obtained from Data-Field 40001, collected until 30 November 2022. Smoking status was defined using Data field 20116.

Statistical analysis
Continuous variables are presented as mean ± standard deviation, and categorical variables as number (percentage). Differences across groups were assessed using independent t-tests or analysis of variance (ANOVA) for continuous variables and Chi-square tests for categorical variables. Sex was determined through the clinical record (UK Biobank Data-Field 31) and confirmed by genetics. Sex was used as a covariate in the analyses.

To evaluate the effect of AI-IR on incident diabetes, logistic regression was performed among participants without diabetes at baseline. Cox regression was not used because exact onset times were unavailable; instead, the duration from baseline to follow-up was included as a covariate. For stratified analyses based on fasting duration prior to blood collection, fasting time was obtained from Data-Field 74. Predictive performance of AI-IR and previously reported metrics was compared using the area under the receiver operating characteristic curve (AUC). Joint effects of AI-IR and previously reported metrics on diabetes incidence were also examined. Cut-offs were defined as BMI of 30, TG/HDL ratio of 3.025, and TyG index of 4.6827, based on previous literatures. Metabolic syndrome (MetS) was defined as the presence of at least three of the following: (1) abdominal obesity (waist circumference ≥ 102 cm in male or ≥ 88 cm in female), (2) elevated triglycerides (≥ 150 mg/dL), (3) reduced HDL cholesterol (< 40 mg/dL in male or < 50 mg/dL in female), (4) elevated blood pressure (≥ 130/85 mmHg), and (5) elevated fasting glucose (≥ 100 mg/dL). Use of medications for triglycerides, HDL cholesterol, blood pressure, or glucose was not considered, as medication use was not included in the computation of AI-IR.

To evaluate the effect of AI-IR or diabetes on the cumulative incidence of 3- and 4-point MACE, overall and cardiovascular mortality, and overall and site-specific cancers, Kaplan–Meier curves were used to compare time to diagnosis across groups, and Cox proportional hazards models were used to estimate hazard ratio (HRs). Models were conducted as crude (unadjusted), adjusted for age and sex, or adjusted for age, sex, and BMI, to account for potential confounding. Age and BMI were treated as continuous variables; sex was binary. To further account for the effect of age on cancer incidence, we also conducted analyses with age as the underlying time variable. This approach ensures that each participant contributes person-time from their age at enrollment until their age at censoring or event, providing a more effective control for confounding by age than including age as a covariate. Joint and interaction effects between AI-IR and smoking status were also evaluated using Cox proportional hazards models, including main effects and an interaction term between AI-IR status and smoking status46,47. All analyses were performed with SAS version 9.4, and a two-sided P value < 0.05 was considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of “Machine learning-predicted insulin resistance is a risk factor for 12 site-specific\nCancers” by CL Lee et al.\nThe paper is generally clear and the methods are solid. I have a few comment though.\nMain comments:\n− All survival analyses: to start follow-up time at enrollment into the UK Biobank is not appropriate. That is not a natural\nstarting point. The time scale of the analysis should be age and follow-up should start at age at enrollment and end at age at\nevent or censoring.\n− That is particularly important for the Kaplan Meier survival curves (e.g. Figure 2) as otherwise age, a very strong frisk factor\nfor all cancers and cardiovascular outcomes, it not accommodated at all in the curves. However, the same comment pertains\nto the Cox regression models, as using age as the scale of analysis adjusts for age the best way possible. Including age as\na covariate is typically not sufficient.\n− How is diabetes defined in the Cox regression and Kaplan Meier analysis? Are diabetes type 1 and 2 combined? I\nassume diabetes was observed, not predicted, is that correct?\n− If yes, as date of diabetes is not know precisely, how was it used when plotting the curves? When starting individuals at\nbaseline one must not use future information on diabetes status to stratify. One can only use baseline diabetes status for the\nstratification. Please clarify what was done. In the Cox model one could incorporate diabetes as a time varying exposure.\n− When looking at individual cancer entities a multiple testing adjustment should be considered. renal pelvis cancer,\nleukemia, and renal pelvis cancer will not be significantly associated with insulin resistance after multiple testing correction.\n− Page 10, lines 175-177: how much were the associations with the predicted insulin resistance score attenuated after\nadjusting for BMI? please comment.\n− Lines 179-181: All lung cancer findings need to be adjusted or stratified by smoking status initially. They are otherwise\nextremely difficult to interpret.\n− Lines 185-192: How was never/previous smoking defined? Are these categories in the UK biobank?\n− Figure 3: the BMI adjusted and unadjusted analyses should be conducted in the same individuals, the unadjusted analysis\nwas done in 94 782 people and the adjusted one in 20938. The differences in effect sizes could be due to differences in the\n2 populations.\n− Analyses should also be stratified by gender numbers permitting.\nMinor point: was the breast cancer analysis restricted to women?", "reviewer_2": "In this article the authors use ML to find predictors of insulin resistance and evaluate its link with cardiovascular disease,\ndiabetes and cancer. The authors state that there is limited epidemiological evidence linking insulin resistance and cancer,\nas the premise of developing their machine learning model. They developed their ML model using NHANES and the Taiwan\nMJ cohort. They propose using the ML model in clinical practice to identify those at risk diabetes, CV disease and cancer.\nTheir results support their conclusions that insulin resistance is associated with an increased risk of cancer.\nA major concern about the data presented in the manuscript is the novelty, and significance of their results with regard to\nadvancing the field. The authors use parameters that are well-known to be associated with insulin resistance, as described\nby Gerald Reaven in 1988. The metabolic syndrome is defined by three of five components hyperglycemia, dyslipidemia\n(elevated TGs and low HDL) and abdominal obesity, or obesity by BMI, in addition to elevated blood pressure as its defining\ncriteria. The metabolic syndrome is a well-known syndrome of insulin resistance, carries with it an increased risk of diabetes,\ncardiovascular disease and cancer (in addition to other conditions such as fatty liver disease, and polycystic ovarian\nsyndrome). The metabolic syndrome is easy to define by clinical criteria, as described in 2009.\nhttps://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.109.192644\nSurprisingly, nowhere in the article do the authors discuss the metabolic syndrome.\nThe authors state that there is a lack of epidemiology data linking insulin resistance to cancer, but there are a number of\nstudies including the Women’s Health Initiative Observational studies, where fasting insulin samples were available, that\nlinked hyperinsulinemia to different cancers, including endometrial cancer.\nhttps://pubmed.ncbi.nlm.nih.gov/18172327/\nhttps://pubmed.ncbi.nlm.nih.gov/18398032/\nThe background literature linking the metabolic syndrome with cancer risk is missing. Below are just a few links to studies,\nmeta-analyses dating back more than 10 years, as well as newer NHANES and China population research.\nhttps://pubmed.ncbi.nlm.nih.gov/23640372/\nhttps://pubmed.ncbi.nlm.nih.gov/23093685/\nhttps://pubmed.ncbi.nlm.nih.gov/24402120/\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191378/\nhttps://acsjournals.onlinelibrary.wiley.com/doi/full/10.1002/cncr.35235\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844618/\nThe authors find that fasting glucose, low HDL and high TGs are predictors of cancer independent of BMI. From the above\nCirculation article, the metabolic syndrome has been defined for 15 years without the need for elevated waist circumference\nor BMI, as it is known that insulin resistance can occur in the absence of obesity. Their finding of insulin resistance as a risk\nfactor independent of BMI is not new.\nThey also describe that obesity is an important factor in driving the metabolic syndrome cancer risk. Obesity is a well-known\nrisk factor for a number of cancers.\nThe authors trained the model in their other manuscript on the NHANES and Taiwan populations. Did they take into account\nthat insulin resistance frequently occurs in Asian populations at lower BMI than in European populations.\nIn the statistical analysis, did the authors correct for multiple comparisons? Are all comparisons between all groups\nstatistically different in Table 1, and supplementary table 1?\nIn the discussion section, beginning at line 254, the authors state that the ML model could be implemented in clinical\npractice at low cost. Given that the we already have clinical criteria to define the metabolic syndrome, that correlates with\ninsulin resistance, and is a well-known risk factor for diabetes and cardiovascular disease, that are inexpensive and easy to\nmeasure in any clinical practice, a critical question that the authors do not address is what the advantage of their ML model\nis over the current metabolic syndrome definition.\nIn the manuscript the authors cite two mendelian randomization studies looking at MR predicted fasting insulin and cancer\nrisk. There are a number of similar MR studies, some of which are summarized in this review article.\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9398112/\nMinor comments\nLine 59:\no Current: “In this regard, it is concerned that insulin resistance and hyperinsulinemia...”\no Suggested: “in this regard, it is theorized that” or “In this regard, it has been proposed that” etc.\nLine 61-62:\no Should be “insulin insulin-like growth factor 1 receptor”\nLine 62:\no Missing article\no Current: “Mechanistic link between inflammation and cancer..”\no Suggested “The mechanistic link between inflammation and cancer..”\nLine 241:\no Current: “Future researches are anticipated to explore..”\no Suggested: “Future research is anticipated to explore”", "reviewer_3": "In their manuscript, “Machine learning-predicted insulin resistance is a risk factor for 12 site-specific cancers” Lee et al use a\npreviously described machine learning predicted insulin sensitivity score to examine the impact of insulin resistance upon\ncancer incidence, arguing that this score could have clinical utility for identifying patients who may benefit from increased\nscreening. The question the authors are asking is very significant to the field because, as they highlight, it has been difficult\nto assess the impact of many of the individual features of systemic metabolism upon cancer risk. Unfortunately, the findings\nas presented need to be contextualized and interpreted in the context of both the obesity and cancer literature as well as the\ncancer metabolism/genetics literature, for as written the authors overstate the potential utility of this approach for cancer risk\nassessment and make claims related to potential causal relationships (e.g.sun exposure in the obese) for which no\nsupportive data is provided, when activation of INSULIN signaling in certain mutational contexts drive senescent\nphenotypes (as a potential example in melanoma). By contrast a discussion of the mutational profiles of Uterine and\nEsophegeal tumors both of which frequently mutate downstream components of the insulin signaling cascade (e.g. PI3K,\nPTEN) may help to explain why these observations are biologically significant. The key issue is that as described\nthroughout their manuscript, the model relies heavily on BMI which is already a known risk factor for 13 tumor types (Lauby-\nSecretan B, 2016 NEJM, PMID: 27557308). This well established observation diminishes the potential utility of the model\nsince BMI is one of its key inputs. It might be helpful to compare how BMI alone performs as a risk factor for these tumor\ntypes, and then potentially examine the impact of insulin sensitivity scores in patients with “normal” BMIs, as that population\nwould not already be identifiable by their physicians as being at elevated risk.\nThe work has significant promise, but as written it again needs to be contextualized in order for readers to be able to\nunderstand its significance, which appears to be reduced from the claims being made by the authors. In many ways as\ncurrently written this feels like a first step, where they have applied their previously developed tool to a second data set,\nconfirming its utility, but then highlight findings that need to be contextualized. These limitations in the data as presented and\nthe need for additional context within the obesity and cancer literature result in the authors overstating the potential utility of\ntheir Machine Learning approach and lead them to overlook, or fail to emphasize some of their findings which are perhaps\nmore novel and may warrant deeper discussion/analysis. In particular the impact of insulin\nsensitivity on lung cancer where they see a signal that may well be independent of obesity\nand its subsequent inflammation.\nSpecific points:\nThe key component missing in this manuscript is an open assessment of how their model compares to the use of BMI alone.\nConceptually it would also benefit if the authors engaged in a frank discussion of how their Machine Learning approach fits\nwithin the extensive literature about how the components of systemic metabolism drive cancer risk in the tumor types they\nare highlighting.\nThe authors fail to build out the logic that underlies their observation in the differences between smokers/non-smokers at the\nend of the results section. It seems as if there would be multiple variables including lung cancer screening which is typically\nonly done in smokers and could account for the differences. Inclusion of an epidemiologist and biostatistician to ensure you\nare not suffering from observation bias might be useful.\nThe claim that obesity might relate to sun exposure is not cited and would need to be backed up to remain (line 220).\nThe statistical methods are not robustly discussed. It would help readers interpret their findings if the authors expanded the\nmethods to include how they are adjusting for Age, Sex, and BMI explicitly.\nThe discussion includes a “future directions” component that discusses further validating the model, which is not the focus of\nthis manuscript but the previous one, so the discussion of correlating hyperinsulinemic-euglycemic clamp data with HOMA-\nIR, and their model seems out of place.\nThe key findings would significantly benefit from validation in independent data sets.\nMinor points:\nThere are several instances where the authors seem to have missed components in their\nediting (eg. Line 188 where it just says reference).\nMethods do not include sufficient detail to evaluate study.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_6": "The paper “Machine learning-predicted insulin resistance is a risk factor for 12 site-specific cancers” examines the link\nbetween insulin resistance, as predicted by a machine learning model, and the risk of certain cancers. Using a machine\nlearning model developed with clinical markers (like BMI, FPG, cholesterol levels), the study assessed insulin resistance\nwithin the UK Biobank cohort and identified associations with 12 specific cancers, such as uterine, kidney, and colon\ncancer. The study found that insulin resistance heightened cancer risk, with both BMI-dependent (e.g., colon cancer) and\nBMI-independent effects (e.g., uterine cancer).\nThis approach suggests that using machine learning-predicted insulin resistance could enhance targeted cancer screening\nand preventive strategies, especially for high-risk individuals identified through metabolic health markers. The findings of\nthis paper could potentially have a significant impact on public health.\nThere are a few concerns and suggestions:\n1. It appears that the authors tested for insulin resistance across various cancers but found no significant association when\nconsidering all cancer types together. However, they identified 12 specific cancers (referred to as site-specific cancers) that\nindividually show a connection to insulin resistance. While this finding is intriguing, it lacks generalizability. I suggest\ncombining the data for these 12 site-specific cancers and re-testing the hypothesis. Since each cancer individually links to\ninsulin resistance, a combined analysis might reveal a stronger overall signal, potentially supporting a broader association. If\nthis additional experiment confirms a link, it could suggest an underlying biological connection across these cancers; if not, it\nmay indicate that insulin resistance acts more as a confounder specific to these cancer types.\n2. The paper’s use of a machine learning approach for predicting insulin resistance could be strengthened by comparisons\nto simpler, established metrics, such as the TG/HDL ratio or TG-glucose index, to evaluate whether the model offers\nsignificant advantages over these alternatives. Additionally, while the authors mention in lines 67-68 that HOMA-IR is\nconsidered the best alternative to the hyperinsulinemic-euglycemic clamp, they do not elaborate on the reasoning behind\nthis assertion, leaving a gap in the justification for their methodological choices.\n3. The study primarily focuses on statistical associations, without exploring the biological mechanisms that might link insulin\nresistance to cancer. A deeper examination of these underlying mechanisms—such as insulin’s role in cellular proliferation\nor its involvement in inflammation pathways—could strengthen the paper by connecting the statistical findings to established\naspects of cancer biology.\n4. While smoking is examined as an interaction effect in lung and bronchial cancers, it is not comprehensively addressed\nacross other cancer types, which may influence results for cancers strongly linked to smoking, especially esophageal\ncancer. Including additional stratification analyses or adjusting for smoking across other cancer subtypes could help\nreduce potential confounding effects and enhance the robustness of the findings."}, "author_response": "Point-by-point response to the editor and reviewers\nLee CL., Hiraike Y., et al. NCOMMS-24-48120-T\nReviewer #1 (Remarks to the Author): Expert in cancer epidemiology and statistics\nReview of “Machine learning-predicted insulin resistance is a risk factor for 12 site-specific\nCancers” by CL Lee et al.\nThe paper is generally clear and the methods are solid. I have a few comments though.\nResponse\nThe authors would like to thank the reviewer for positively evaluating our manuscript. To\naddress concerns raised by this reviewer and strengthen or fine-tune our conclusion, we\nperformed additional analyses. Regarding the effect of age on the incidence of cancer, we\nconfirmed that the incidence of cancer increased with age. Moreover, both predicted insulin\nresistance (AI-IR) and diabetes were associated with a higher incidence of cancer across\nall age group, indicating that AI-IR is the risk factor for cancer regardless of age. We further\naddressed all other specific comments and revised the manuscript accordingly.\nMain comments:\n− All survival analyses: to start follow-up time at enrollment into the UK Biobank is not\nappropriate. That is not a natural starting point. The time scale of the analysis should be age\nand follow-up should start at age at enrollment and end at age at event or censoring.\nResponse\nThe authors feel that a Kaplan-Meier analysis starting from age at enrollment is a kind of\nstandard analysis in the community. Having said that, we sincerely agree that age is a very\nstrong risk factor for all cancers and cardiovascular outcomes and we need to consider the\neffect of age when evaluating the effect of AI-IR. To address this point, we performed multiple\nadditional analyses. Please refer to our response to the next comment.\n− That is particularly important for the Kaplan Meier survival curves (e.g. Figure 2) as\notherwise age, a very strong risk factor for all cancers and cardiovascular outcomes, it not\naccommodated at all in the curves. However, the same comment pertains to the Cox\nregression models, as using age as the scale of analysis adjusts for age the best way\npossible. Including age as a covariate is typically not sufficient.\nResponse\ncancer, we performed multiple additional analyses. First, we evaluated the incidence of the\ncomposite cancers per 1000 person-years across different age groups and showed that AI-\nIR were associated with a higher incidence of the composite cancers across different age\ngroups (page 14, line 221 in the revised manuscript).\n“Cancer risk is well known to increase with age29,30. Indeed, when we stratified\nparticipants by age at enrollment (40 to 69 years old), the incidence of the composite\ncancers per 1000 person-years increased with age (Supplementary Fig. 4a).\nFurthermore, both AI-IR and diabetes were associated with a higher incidence of the\ncomposite cancers across all age groups (Supplementary Fig. 4a), indicating that AI-\nIR is the risk factor for the composite cancers across different age groups.”\nOf note, we defined composite cancers by merging 10 cancer types (common to both\nmales and females) whose risks were either significantly increased (kidney, esophagus,\npancreas, colon) or nominally increased (renal pelvis, small intestine, stomach, liver and GB,\ncolon, and bronchial and lung) by AI-IR (page 12, line 200 in the revised manuscript).\nWe also performed Kaplan-Meier analyses stratified by participants’ age at enrollment\n(page 14, line 226 in the revised manuscript).\n“Kaplan-Meier analyses also revealed that significant difference between participants\nwith diabetes, those without diabetes but positive for AI-IR, and those without diabetes\nand negative for AI-IR on the cumulative incidence of the composite cancers are\nconsistent across different age groups: <50 years old, 50–59 years old, and ≥60 years\nold at enrollment (Supplementary Fig. 4b-d).\nalso included age-adjusted analyses (page 7, line 112 in the revised manuscript).\n“Of note, AI-IR was associated with a significantly higher risk of diabetes onset and\nhospital admission with diabetes after adjusting for age and sex, and even after\nadjusting for age, sex, and BMI, indicating that our machine learning-based prediction\nmodel is able to capture a BMI-independent effect of insulin resistance\n(Supplementary Fig. 1a-b).”\n− How is diabetes defined in the Cox regression and Kaplan Meier analysis? Are diabetes\ntype 1 and 2 combined? I assume diabetes was observed, not predicted, is that correct?\nResponse\nIn response to this comment, we added more description into the method section (page\n24, line 406 in the revised manuscript). We used observed phenotype of diabetes for the\ncurrent study, and we included all subjects with diabetes for analyses.\n“Observed phenotypes of diabetes was used for the current study. Individuals with\nHbA1c > 6.5% at enrollment and/or self-repot of physician’s diagnosis were used to\ndefine diabetes. For short-term follow-up analysis in the UK Biobank, HbA1c at the\n3\nfollow-up and/or self-repot of physician’s diagnosis were used to define new-onset of\ndiabetes. For long-term follow-up analysis using the linkage to the NHS medical record\nsystem, diagnosis registered in NHS was used for the analysis. Diagnosis date was\ndefined as the date of admission with diagnosis of diabetes. We included all subjects\nwith diabetes for aforementioned analyses.”\n− If yes, as date of diabetes is not know precisely, how was it used when plotting the curves?\nWhen starting individuals at baseline one must not use future information on diabetes status\nto stratify. One can only use baseline diabetes status for the stratification. Please clarify what\nwas done. In the Cox model one could incorporate diabetes as a time varying exposure.\nResponse\nWe used baseline status (participants without diabetes and negative for AI-IR, those\nwithout diabetes but positive for AI-IR, and those with diabetes) for stratification (page 11,\nline 167 in the revised manuscript).\n“To investigate the effect of AI-IR on the incidence of cancer, we leveraged the linkage\nbetween the UK Biobank and the NHS medical record system and examined the\nincidence of cancer among participants who were cancer free at the baseline visit (N\n= 372,395). We compared participants without diabetes and negative for AI-IR (N =\n256,685), those without diabetes but positive for AI-IR (N = 94,782), and those with\ndiabetes (N = 20,928). Of 372,395 participants who were cancer free at baseline,\n51,193 developed cancer (Supplementary Table 3).”\n− When looking at individual cancer entities a multiple testing adjustment should be\nconsidered. renal pelvis cancer, leukemia, and renal pelvis cancer will not be significantly\nassociated with insulin resistance after multiple testing correction.\nResponse\nThe authors would like to thank the reviewer for this really important comment. We\nperformed Bonferroni correction after multiple testing in the revised manuscript (page 11, line\n177 in the revised manuscript)\n“However, when we looked at individual types of cancer, both AI-IR and diabetes were\nassociated with a significantly higher risk of incidence of multiple cancer types (The\nresults of the 25 common cancers are shown in Fig. 3, and the complete list can be\nfound in Supplementary Table 4). We examined the incidence of 36 common cancers\n(both male and female), 4 cancer types specific to males, and 3 cancer types specific\nto females. The Bonferroni-corrected P value for significance was 0.05/43 = 1.163 x\n10-3.”\nThe results were mentioned in page 12, line 193 in the revised manuscript.\n“When we considered Bonferroni correction, AI-IR was associated with a higher\nincidence of uterine, kidney, esophagus, pancreas, colon, and breast cancers.”\n− Page 10, lines 175-177: how much were the associations with the predicted insulin\nresistance score attenuated after adjusting for BMI? please comment.\nResponse\nIn the revised manuscript, we defined composite cancers by merging 10 cancer types\n(common to both males and females) whose risks were either significantly or nominally\nincreased by AI-IR and looked at the effect of AI-IR on the incidence of the composite cancers\n(page 13, line 203 in the revised manuscript). We observed that approximately 36% of the\neffect of AI-IR was attenuated after adjusting for BMI.\n“a Kaplan-Meier plot revealed a significant difference between participants with\ndiabetes, those without diabetes but positive for AI-IR, and those without diabetes and\nnegative for AI-IR, on the cumulative incidence of the composite cancers (Fig. 4a).\nThe HR adjusted for age and sex in participants without diabetes but positive for AI-\nIR was 1.25 (95% CI, 1.20-1.31; P < 1 x 10-11), and that in participants with diabetes\nwas 1.40 (95% CI, 1.31-1.50; P < 1 x 10-11). When we adjusted also for BMI, The HR\n5\nin participants without diabetes but positive for AI-IR was 1.16 (95% CI, 1.10-1.22; P\n= 1.2 x 10-8), suggesting that approximately 36% of AI-IR’ effect is mediated through\nBMI.”\n− Lines 179-181: All lung cancer findings need to be adjusted or stratified by smoking status\ninitially. They are otherwise extremely difficult to interpret.\nResponse\nIn response to this comment, we additionally examined the effect of AI-IR on the incidence\nof bronchial and lung cancer as well as composite cancers while adjusting for smoking status\nas well as age, sex, and BMI, which is mentioned in page 15, line 247 in the revised\nmanuscript.\n“Lung cancer is strongly associated with smoking, the leading risk factor for the global\nburden of cancer31. We observed that the effect of AI-IR on the incidence of bronchial\nand lung cancer is significant even when we adjusted also for smoking status (never\nsmoker, previous smoker, and current smoker); The same is true for the incidence of\nthe composite cancers (Supplementary Fig. 5a-b).”\n− Lines 185-192: How was never/previous smoking defined? Are these categories in the UK\nbiobank?\nYes. Data field 20116 was used to define smoking status (page 25, line 425 in the revised\nmanuscript).\n− Figure 3: the BMI adjusted and unadjusted analyses should be conducted in the same\nindividuals, the unadjusted analysis was done in 94,782 people and the adjusted one in\n20,938. The differences in effect sizes could be due to differences in the 2 populations.\nResponse\nCaption for Figure 4A (Figure 4C in the revised manuscript) was incorrect. Both BMI-\nunadjusted and BMI-adjusted analysis was performed in 94,782 participants without diabetes\nbut positive for AI-IR. The BMI adjusted and unadjusted analyses were conducted in the\nsame individuals.\n− Analyses should also be stratified by gender numbers permitting.\nResponse\nIn response to this comment, we additionally performed sex-specific analysis looking at\nthe effect of AI-IR on the incidence of the composite cancers (page 13, line 210 in the revised\nmanuscript).\n“Sex-specific analysis revealed that the impact of AI-IR on the incidence of the\ncomposite cancers remained consistent in both males and females (Supplementary\nFig 2a, b).”\nResponse\nYes, the breast cancer analysis was restricted to women. We found that AI-IR significantly\nincreased the incidence of both composite cancers (common to both males and females)\nand female-specific cancers, including uterine and breast cancers (page 13, line 212 in the\nrevised manuscript).\n“When we combined female-specific uterine and breast cancers whose risks were\nsignificantly increased by AI-IR, we observed that effect the HR adjusted for age in\nparticipants without diabetes but positive for AI-IR was 1.26 (95% CI, 1.20-1.32; P < 1\nx 10-11), which was comparable to that in participants with diabetes (HR, 1.23; 95% CI,\n1.11-1.36; P < 3.9 x 10-5) (Fig. 4b).”\nIn this article the authors use ML to find predictors of insulin resistance and evaluate its link\nwith cardiovascular disease, diabetes and cancer. The authors state that there is limited\nepidemiological evidence linking insulin resistance and cancer, as the premise of developing\ntheir machine learning model. They developed their ML model using NHANES and the\nTaiwan MJ cohort. They propose using the ML model in clinical practice to identify those at\nrisk diabetes, CV disease and cancer.\nTheir results support their conclusions that insulin resistance is associated with an increased\nrisk of cancer.\nResponse\nThe authors would like to thank this reviewer for constructive comments to improve our\nmanuscript. In response to the specific comments below, we performed additional analyses\nand revised the manuscript to better support our conclusion. Specifically, we directly\ncompared our prediction model, AI-IR and previously established metrics including BMI,\nMetabolic syndrome, TG/HDL ratio and TyG index. We conclude that AI-IR is the single best\nmetric for predicting both diabetes and composite cancer incidence.\nA major concern about the data presented in the manuscript is the novelty, and significance\nof their results with regard to advancing the field. The authors use parameters that are well-\nknown to be associated with insulin resistance, as described by Gerald Reaven in 1988. The\nmetabolic syndrome is defined by three of five components hyperglycemia, dyslipidemia\n(elevated TGs and low HDL) and abdominal obesity, or obesity by BMI, in addition to elevated\nblood pressure as its defining criteria. The metabolic syndrome is a well-known syndrome of\ninsulin resistance, carries with it an increased risk of diabetes, cardiovascular disease and\ncancer (in addition to other conditions such as fatty liver disease, and polycystic ovarian\nsyndrome). The metabolic syndrome is easy to define by clinical criteria, as described in\n2009.\nhttps://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.109.192644\nSurprisingly, nowhere in the article do the authors discuss the metabolic syndrome.\nResponse\nThe authors would like to thank the reviewer for this important and constructive comment.\nIn response to this comment, we refer to the metabolic syndrome in the introduction and cite\nthe indicated article (page 5, line 72 in the revised manuscript).\n“Similarly, metabolic syndrome, which is linked to visceral adiposity and its associated\n9\ninsulin resistance11, is also associated with a higher risk of cancer12–17.”\nMore importantly, in response to this constructive reviewer comment, we directly compared\nAI-IR and Metabolic syndrome (MetS) regarding the predictive capabilities of the incidence\nof diabetes. We demonstrated that AI-IR is the best predictor of diabetes incidence compared\nto BMI, MetS, TG/HDL ratio, and TyG index (page 9, line 134 in the revised manuscript).\n“To examine whether AI-IR outperforms previously established simpler metrics of\nobesity, metabolic syndrome, and insulin resistance, we compared the predictive\ncapabilities of AI-IR, BMI, metabolic syndrome (MetS), TG/HDL ratio, and TyG index\nfor the incidence of diabetes among individuals without diabetes at baseline during\nthe follow-up period. TG/HDL ratio24,25 and TyG index26–28 has been reported as a\nsurrogate markers of insulin resistance. Area under the curve (AUC) of the receiver\noperating characteristic (ROC) curve was highest for AI-IR (0.798), followed by MetS\n(0.748), BMI (0.721), TyG index (0.703), and Tg/HDL ratio (0.702), indicating the AI-\nIR exhibits the best predictive capability among these metrics (Fig. 2a).”\n(page 9, line 142 in the revised manuscript)\n“Next, we classified individuals without diabetes at baseline into four groups based on\nAI-IR status and the status of previously established metrics.”\n(page 10, line 153 in the revised manuscript)\nwhen we classified individuals without diabetes into four groups based on AI-IR status\nand MetS, the incidence of diabetes significantly higher (P = 1.55 x 10-3) for AI-IR\nsingle-positive group (OR, 6.85; 95% CI, 4.35-10.80; P = 1.2 x 10-16) compared to\nMetS single-positive group (OR, 3.14; 95% CI, 2.07-4.77; P = 6.8 x 10-8). Again, the\nincidence of diabetes was significantly higher (P = 2.19 x 10-13) for AI-IR and MetS\ndouble-positive group (OR, 11.71; 95% CI 8.53-16.06; P < 1 x 10-20) compared to MetS\nsingle positive group (Fig. 2c).\ncomposite cancer (page 16, line 273 in the revised manuscript).\n“Finally, to examine whether AI-IR provides a better prediction of the composite cancer\nincidence than previously established metrics such as BMI, MetS, TG/HDL ratio, and\nTyG index, we classified participants who were free of diabetes and cancer at baseline\ninto four groups based on their AI-IR status and the status of these metrics.”\n(page 17, line 284 in the revised manuscript)\n“When considering AI-IR and MetS, the AI-IR single positive group (HR, 1.21; 95% CI\n1.12-1.31; P = 2.1 x 10-6) and the MetS single positive group (HR, 1.19; 95% CI 1.12-\n1.26; P = 1.8 x 10-9) exhibited comparable HRs (P = 0.649) for the incidence of the\ncomposite cancers. However, the incidence of composite cancers was significantly\nhigher (P = 1.49 × 10⁻⁴) in the AI-IR and MetS double-positive group (HR, 1.34; 95%\nCI 1.27-1.40; P < 1 × 10⁻¹¹) compared to the MetS single-positive group (Fig. 5b).”\n(page 18, line 297 in the revised manuscript)\n“Altogether, these results indicate that AI-IR enables improved cancer risk stratification\ncompared to previously established metrics.”\nbut there are a number of studies including the Women’s Health Initiative Observational\nstudies, where fasting insulin samples were available, that linked hyperinsulinemia to\ndifferent cancers, including endometrial cancer.\nhttps://pubmed.ncbi.nlm.nih.gov/18172327/\nhttps://pubmed.ncbi.nlm.nih.gov/18398032/\nResponse\nIn response to this comment, we additionally cite indicated papers in the context of insulin\nresistance and cancer (page 5, line 74 in the revised manuscript)\n“However, large-scale epidemiological evidence directly linking insulin resistance and\ncancer is limited, with only a few studies examining the incidence of endometrial and\ncolorectal cancer using high fasting insulin as a surrogate marker for insulin\nresistance18,19, because insulin resistance is rarely evaluated in patients, except for\nthose being treated in specialized diabetes clinics.”\nThe background literature linking the metabolic syndrome with cancer risk is missing. Below\nare just a few links to studies, meta-analyses dating back more than 10 years, as well as\n12\nnewer NHANES and China population research.\nhttps://pubmed.ncbi.nlm.nih.gov/23640372/\nhttps://pubmed.ncbi.nlm.nih.gov/23093685/\nhttps://pubmed.ncbi.nlm.nih.gov/24402120/\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191378/\nhttps://acsjournals.onlinelibrary.wiley.com/doi/full/10.1002/cncr.35235\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844618/\nResponse\nIn response to this comment, we refer to the metabolic syndrome in the introduction and\ncite the indicated articles (page 5, line 72 in the revised manuscript). We would like to\nappreciate this suggestion to improve our manuscript.\n“Similarly, metabolic syndrome, which is linked to visceral adiposity and its associated\ninsulin resistance11, is also associated with a higher risk of cancer12–17.”\nThe authors find that fasting glucose, low HDL and high TGs are predictors of cancer\nindependent of BMI. From the above Circulation article, the metabolic syndrome has been\ndefined for 15 years without the need for elevated waist circumference or BMI, as it is known\nthat insulin resistance can occur in the absence of obesity. Their finding of insulin resistance\nas a risk factor independent of BMI is not new.\nThey also describe that obesity is an important factor in driving the metabolic syndrome\ncancer risk. Obesity is a well-known risk factor for a number of cancers.\nResponse\nWe acknowledge that previous literatures observed that insulin resistance is linked to\nincreased risk of cancer, sometimes in the absence of obesity. In the current manuscript,\nwe found that our machine-learning based prediction model of insulin resistance, AI-IR,\noutperforms BMI alone as well as previously established metrics including MetS, TG/HDL\nratio and TyG index regarding the incidence of new-onset diabetes and the composite cancer.\n(page 20, line 340 in the revised manuscript).\n“We acknowledge that the BMI-independent effect of insulin resistance on increased\ncancer risk is not a new finding. While the definitions of MetS, TG/HDL ratio, and TyG\nindex do not include BMI, MetS—originally developed as an indicator of visceral\nadiposity—has been reported as a risk factor for certain types of cancer12–17. Indeed,\nwe observed that higher BMI (≥30) alone was not associated with an increased risk of\ncomposite cancer (Fig. 5a). AI-IR was a better predictor of composite cancer\n13\ncompared to BMI and TyG index (Fig. 5a, 5d), but its predictive capability was\ncomparable to that of MetS and TG/HDL ratio (Fig. 5b and 5c). Having said that, since\nAI-IR demonstrated the best predictive capability for the incidence of diabetes among\nthe examined metrics, including MetS and TG/HDL ratio (Fig. 2), and diabetes itself is\na significant risk factor for the composite cancers (Fig. 4a), we argue that AI-IR is the\nsingle best metric for predicting both diabetes and composite cancer incidence.\nFurthermore, AI-IR captures both BMI-dependent and BMI-independent effects of\ninsulin resistance.”\nWe also add discussion in page 21, line 355 in the revised manuscript.\n“Our prediction model is based on nine commonly measured clinical parameters, and\nwe demonstrated that it outperforms previously established simpler metrics—\nincluding BMI, MetS, TG/HDL ratio, and TyG index—in predicting both diabetes and\ncomposite cancer incidence. Our model provides “digital biomarker of insulin\nresistance” which merges all parameters currently established as a component of\ninsulin resistance into a single metric, which will be utilized for identification of high-\nrisk individuals and focused screening of diabetes, cardiovascular disease, and\ncancer.”\nThe authors trained the model in their other manuscript on the NHANES and Taiwan\npopulations. Did they take into account that insulin resistance frequently occurs in Asian\npopulations at lower BMI than in European populations.\nResponse\nWe included ethnicity (Asian versus non-Asian) into our prediction model when we\n14\ndeveloped prediction model (eClinicalmedicine 2023). Therefore, our model did take into\naccount that insulin resistance frequently occurs in Asian populations at lower BMI than in\nEuropean populations. While current manuscript largely refers to non-Asian populations, we\nwill conduct dedicated studies to examine the effect of our model, AI-IR, on the incidence of\ncancer in Asian populations.\nIn the statistical analysis, did the authors correct for multiple comparisons? Are all\ncomparisons between all groups statistically different in Table 1, and supplementary table 1?\nResponse\nThe authors would like to thank the reviewer for this really important comment. We\nperformed Bonferroni correction after multiple testing in the revised manuscript (Page 11,\nline 177 in the revised manuscript).\n“However, when we looked at individual types of cancer, both AI-IR and diabetes were\nassociated with a significantly higher risk of incidence of multiple cancer types (The\nresults of the 25 common cancers are shown in Fig. 3, and the complete list can be\nfound in Supplementary Table 4). We examined the incidence of 36 common cancers\n(both male and female), 4 cancer types specific to males, and 3 cancer types specific\nto females. The Bonferroni-corrected P value for significance was 0.05/43 = 1.163 x\n10-3.”\nThe results were mentioned in page 12, line 193 in the revised manuscript.\n15\n“When we considered Bonferroni correction, AI-IR was associated with a higher\nincidence of uterine, kidney, esophagus, pancreas, colon, and breast cancers.”\nIn the discussion section, beginning at line 254, the authors state that the ML model could\nbe implemented in clinical practice at low cost. Given that the we already have clinical criteria\nto define the metabolic syndrome, that correlates with insulin resistance, and is a well-known\nrisk factor for diabetes and cardiovascular disease, that are inexpensive and easy to measure\nin any clinical practice, a critical question that the authors do not address is what the\nadvantage of their ML model is over the current metabolic syndrome definition.\nResponse\nWe acknowledge that defining MetS is inexpensive and easy in clinical practice. Having\nsaid that, as we already mentioned in response to the previous points, the current study\ndemonstrated that AI-IR outperforms previously established metrics including MetS in terms\nof the prediction of diabetes. Regarding the prediction of the incidence of the composite\ncancers, the capability of AI-IR was comparable to that of MetS. However, since diabetes\nitself is a significant risk factor for the composite cancers, collectively, we argue that AI-IR is\nthe single best metric for predicting both diabetes and composite cancer incidence.\nWe revised the discussion by removing the argument regarding cost and ease of use while\nincorporating a comparison with previously established metrics (page 21, line 353 in the\nrevised manuscript).\n“The strength of our study is that, to our knowledge, it is the first to leverage the use\nof a machine learning-based prediction model of insulin resistance to look at the effect\nof insulin resistance on the incidence of cancer at population scale. Our prediction\nmodel is based on nine commonly measured clinical parameters, and we\ndemonstrated that it outperforms previously established simpler metrics—including\nBMI, MetS, TG/HDL ratio, and TyG index—in predicting both diabetes and composite\ncancer incidence. Our model would provide “digital biomarker of insulin resistance”\nwhich merges all parameters currently established as a component of insulin\nresistance into a single metric, which will be utilized for identification of high-risk\nindividuals and focused screening of diabetes, cardiovascular disease, and cancer.“\nIn the manuscript the authors cite two mendelian randomization studies looking at MR\npredicted fasting insulin and cancer risk. There are a number of similar MR studies, some of\nwhich are summarized in this review article.\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9398112/\nIn response to this comment, we additionally cite the indicated review article (page 19, line\n319 in the revised manuscript).\n“In line with our observations, a Mendelian randomization using 18 variants associated\nwith fasting insulin levels found that genetically predicted higher fasting insulin levels\nincreased the risk of kidney, pancreas, and lung cancer4. Similar Mendelian\nrandomization studies have been replicated and summarized elsewhere33.”\nMinor comments\nLine 59:\no Current: “In this regard, it is concerned that insulin resistance and hyperinsulinemia...”\no Suggested: “in this regard, it is theorized that” or “In this regard, it has been proposed that”\netc.\nResponse\nWe modified to “In this regard, it has been proposed that…”. Thank you for your suggestion.\nLine 61-62:\no Should be “insulin insulin-like growth factor 1 receptor”\nResponse\nWe corrected.\nLine 62:\no Missing article\no Current: “Mechanistic link between inflammation and cancer..”\no Suggested “The mechanistic link between inflammation and cancer..”\nResponse\nWe corrected the sentence.\nLine 241:\no Current: “Future researches are anticipated to explore..”\no Suggested: “Future research is anticipated to explore”\nWe substantially revised the discussion section to address all the reviewers' concerns.\nDuring the revision, we removed the corresponding statement to better focus on the main\nscope of our manuscript, particularly the comparison of AI-IR with previously established\nmetrics. Nevertheless, we sincerely appreciate the reviewer’s careful and thorough reading\nof our work.\ncancer\nIn their manuscript, “Machine learning-predicted insulin resistance is a risk factor for 12 site-\nspecific cancers” Lee et al use a previously described machine learning predicted insulin\nsensitivity score to examine the impact of insulin resistance upon cancer incidence, arguing\nthat this score could have clinical utility for identifying patients who may benefit from\nincreased screening. The question the authors are asking is very significant to the field\nbecause, as they highlight, it has been difficult to assess the impact of many of the\nindividual features of systemic metabolism upon cancer risk. Unfortunately, the findings\nas presented need to be contextualized and interpreted in the context of both the obesity and\ncancer literature as well as the cancer metabolism/genetics literature, for as written the authors\noverstate the potential utility of this approach for cancer risk assessment and make claims\nrelated to potential causal relationships (e.g. sun exposure in the obese) for which no\nsupportive data is provided, when activation of INSULIN signaling in certain mutational\ncontexts drive senescent phenotypes (as a potential example in melanoma). By contrast a\ndiscussion of the mutational profiles of Uterine and Esophegeal tumors both of which\nfrequently mutate downstream components of the insulin signaling cascade (e.g. PI3K,\nPTEN) may help to explain why these observations are biologically significant. The key issue\nis that as described throughout their manuscript, the model relies heavily on BMI which is\nalready a known risk factor for 13 tumor types (Lauby-Secretan B, 2016 NEJM,\nPMID:27557308). This well established observation diminishes the potential utility of the\nmodel since BMI is one of its key inputs. It might be helpful to compare how BMI alone\nperforms as a risk factor for these tumor types, and then potentially examine the impact of\ninsulin sensitivity scores in patients with “normal” BMIs, as that population would not already\nbe identifiable by their physicians as being at elevated risk.\nThe work has significant promise, but as written it again needs to be contextualized in order\nfor readers to be able to understand its significance, which appears to be reduced from the\nclaims being made by the authors. In many ways as currently written this feels like a first step,\nwhere they have applied their previously developed tool to a second data set, confirming its\nutility, but then highlight findings that need to be contextualized. These limitations in the data\nas presented and the need for additional context within the obesity and cancer literature\nresult in the authors overstating the potential utility of their Machine Learning approach and\nlead them to overlook, or fail to emphasize some of their findings which are perhaps more\nnovel and may warrant deeper discussion/analysis. In particular the impact of insulin\nsensitivity on lung cancer where they see a signal that may well be independent of obesity\nand its subsequent inflammation.\nThe authors would like to thank this reviewer for appreciating the importance of the\nquestion we are asking in the current study. We also thank specific comments below to\nimprove our manuscript. To address concerns raised by this reviewer, for example, we\ndirectly compared AI-IR with BMI and other previously established metrics regarding the\nprediction of the incidence of diabetes as well as cancer. We demonstrated that AI-IR\noutperforms BMI and other metrics.\nAs this reviewer mentions, it has been difficult to assess the impact of many of the\nindividual features of systemic metabolism upon cancer risk. Our machine learning-based\nprediction model of insulin resistance, AI-IR, can provide “digital biomarker of insulin\nresistance” which merges all parameters currently established as a component of insulin\nresistance into a single model. Our model includes both BMI-dependent and -independent\neffect of insulin resistance.\nSpecific points:\nThe key component missing in this manuscript is an open assessment of how their model\ncompares to the use of BMI alone. Conceptually it would also benefit if the authors engaged\nin a frank discussion of how their Machine Learning approach fits within the extensive\nliterature about how the components of systemic metabolism drive cancer risk in the tumor\ntypes they are highlighting.\nResponse\nIn response to this important comment, we directly compared AI-IR with BMI alone and\nother previously established metrics (MetS, TG/HDL ratio, and TyG index) regarding the\npredictive capabilities of the incidence of diabetes. We demonstrated that AI-IR is the best\npredictor of diabetes incidence compared to BMI, MetS, TG/HDL ratio, and TyG index (Fig.\n2a and page 9, line 134 in the revised manuscript).\n“To examine whether AI-IR outperforms previously established simpler metrics of\nobesity, metabolic syndrome, and insulin resistance, we compared the predictive\ncapabilities of AI-IR, BMI, metabolic syndrome (MetS), TG/HDL ratio, and TyG index\nfor the incidence of diabetes among individuals without diabetes at baseline during\nthe follow-up period. TG/HDL ratio24,25 and TyG index26–28 has been reported as a\nsurrogate markers of insulin resistance. Area under the curve (AUC) of the receiver\noperating characteristic (ROC) curve was highest for AI-IR (0.798), followed by MetS\n(0.748), BMI (0.721), TyG index (0.703), and Tg/HDL ratio (0.702), indicating the AI-\nWe also compared AI-IR and BMI regarding the prediction of the incidence of the\ncomposite cancer (page 16, line 273 in the revised manuscript).\n“Finally, to examine whether AI-IR provides a better prediction of the composite cancer\nincidence than previously established metrics such as BMI, MetS, TG/HDL ratio, and\nTyG index, we classified participants who were free of diabetes and cancer at baseline\ninto four groups based on their AI-IR status and the status of these metrics. When we\nconsider AI-IR and BMI, we observed that the incidence of the composite cancers was\nsignificantly higher (P = 2.28 x 10-2) for the AI-IR single-positive group (HR, 1.18; 95%\nCI, 1.11-1.26; P < 9.5 x 10-8) compared to the BMI single-positive group (HR, 1.04;\n95% CI, 0.95-1.15; P = 0.38). BMI of 30 or higher alone did not significantly increase\nthe incidence of the composite cancers. We also observed that the incidence of the\ncomposite cancers was significantly higher (P = 2.01 x 10-5) for the AI-IR and BMI\ndouble-positive group (HR, 1.30; 95% CI 1.24-1.37; P < 1 x 10-11) compared to the\nBMI single positive group, indicating that AI-IR enables significantly improved cancer\nrisk stratification among obese individuals (Fig. 5a).”\nAlso, we discussed the effect of high TG and low HDL on the incidence of lung cancer\n(page 20, line 332 in the revised manuscript).\n“Considering that feature importance of our model was highest for higher BMI,\nfollowed by higher FPG, lower HDL, and higher TG23, the effect of AI-IR on bronchial\nand lung cancer is BMI-independent but it may be dependent on FPG, HDL, and TG.\nSupportively, studies reported that both high TG and low HDL are positively associated\nwith lung cancer risk39,40.”\nbetween smokers/non-smokers at the end of the results section. It seems as if there would\nbe multiple variables including lung cancer screening which is typically only done in smokers\nand could account for the differences. Inclusion of an epidemiologist and biostatistician to\nensure you are not suffering from observation bias might be useful.\nResponse\nIn response to this comment (and also in response to the comment from reviewer 1), we\nadditionally examined the effect of AI-IR on the incidence of bronchial and lung cancer as\nwell as composite cancers while adjusting for smoking status, which is mentioned in page\n15, line 247 in the revised manuscript.\n“Lung cancer is strongly associated with smoking, the leading risk factor for the global\nburden of cancer31. We observed that the effect of AI-IR on the incidence of bronchial\nand lung cancer is significant even when we adjusted also for smoking status (never\nsmoker, previous smoker, and current smoker); The same is true for the incidence of\nthe composite cancers (Supplementary Fig. 5a-b).”\nRegarding the joint effect analysis (Supplementary Fig. 5c in the revised manuscript), we\nfound that the incidence of lung cancer is significantly different in the same smoking status\ngroup (i.e. previous smoker), which is unlikely derived from the difference in the frequency of\nscreening.\nThe claim that obesity might relate to sun exposure is not cited and would need to be backed\nup to remain (line 220).\nResponse\nThe authors would like to thank the reviewer for very carefully reading our manuscript. To\nbetter focus on the main scope of our manuscript, we decided to remove the claim in the\nrevised manuscript.\nThe statistical methods are not robustly discussed. It would help readers interpret their\nfindings if the authors expanded the methods to include how they are adjusting for Age, Sex,\n22\nand BMI explicitly.\nResponse\nIn response to this constructive comment, we added more detail into the method section\n(page 26, line 444 in the revised manuscript).\n“When indicated, logistic regression and Cox proportional hazards models were\nadjusted for key covariates, including age, sex, and BMI, to account for potential\nconfounding factors. To evaluate the robustness of AI-IR's effects across different\ncovariate combinations, we conducted analyses in the following sequential steps: (1)\ncrude (unadjusted), (2) adjusted for age and sex, and (3) adjusted for age, sex, and\nBMI. Age (in years) and BMI (kg/m2) were treated as continuous variables, while sex\n(male or female) was included as a binary variable.”\nThe discussion includes a “future directions” component that discusses further validating the\nmodel, which is not the focus of this manuscript but the previous one, so the discussion of\ncorrelating hyperinsulinemic-euglycemic clamp data with HOMA-IR, and their model seems\nout of place.\nResponse\nThe author would like to appreciate this constructive comment. We removed our discussion\non the correlation between clamp and HOMA-IR.\nThe key findings would significantly benefit from validation in independent data sets.\nResponse\nThe authors strongly agree with this point. However, obtaining access to independent\ndatasets within a reasonable timeframe for revision is not feasible. We plan to conduct a\ndedicated study to examine the effect of AI-IR on cancer incidence in the Asian population.\nMinor points:\nThere are several instances where the authors seem to have missed components in their\nediting (eg. Line 188 where it just says reference).\nResponse\nThank you for your comment. That “reference” in the original manuscript referred to\nreference point for hazard ratio. During the revision, we thoroughly reviewed and edited the\n23\nentire manuscript to enhance readability.\nMethods do not include sufficient detail to evaluate study.\nResponse\nIn response to this reviewer comment, we added more description regarding the definition\nof diabetes in this study (page 24, line 406 in the revised manuscript), comparison of the\ncapability of AI-IR with previously reported metrics (page 26, line 437 in the revised\nmanuscript), and adjustment of logistic regression and Cox proportional hazards models\n(page 26, line 444 in the revised manuscript).\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse\nThe authors would like to appreciate your time and effort to review our manuscript.\nThe paper “Machine learning-predicted insulin resistance is a risk factor for 12 site-specific\ncancers” examines the link between insulin resistance, as predicted by a machine learning\nmodel, and the risk of certain cancers. Using a machine learning model developed with\nclinical markers (like BMI, FPG, cholesterol levels), the study assessed insulin resistance\nwithin the UK Biobank cohort and identified associations with 12 specific cancers, such as\nuterine, kidney, and colon cancer. The study found that insulin resistance heightened cancer\nrisk, with both BMI-dependent (e.g., colon cancer) and BMI-independent effects (e.g., uterine\ncancer).\nThis approach suggests that using machine learning-predicted insulin resistance could\nenhance targeted cancer screening and preventive strategies, especially for high-risk\nindividuals identified through metabolic health markers. The findings of this paper could\npotentially have a significant impact on public health.\nResponse\nThe authors would like to the reviewer for appreciating the impact of our machine learning\nbased prediction model of insulin resistance on public health such as targeted cancer\nscreening and preventive strategies. In response to the specific comments below, for\nexample, we defined composite cancers by merging 10 cancer types (common to both males\nand females) whose risks were either significantly or nominally increased by AI-IR and\nevaluated the effect of AI-IR on the incidence of the composite cancer event to enhance\ngeneralizability of our finding. Furthermore, we directly compared AI-IR with previously\nestablished simpler metrics including BMI, MetS, TG/HDL ratio, and TyG index regarding the\npredictive capabilities of the incidence of diabetes as well as the composite cancer.\nThere are a few concerns and suggestions:\n1. It appears that the authors tested for insulin resistance across various cancers but found\nno significant association when considering all cancer types together. However, they\nidentified 12 specific cancers (referred to as site-specific cancers) that individually show a\nconnection to insulin resistance. While this finding is intriguing, it lacks generalizability. I\nsuggest combining the data for these 12 site-specific cancers and re-testing the hypothesis.\nSince each cancer individually links to insulin resistance, a combined analysis might reveal\na stronger overall signal, potentially supporting a broader association. If this additional\nexperiment confirms a link, it could suggest an underlying biological connection across these\ncancers; if not, it may indicate that insulin resistance acts more as a confounder specific to\nthese cancer types.\nThe authors would like to thank the reviewer for this really constructive comment. This\ncomment was very helpful to improve our manuscript and better support our conclusion.\nAccording to this reviewer comment, we defined composite cancers by merging 10 cancer\ntypes (common to both males and females) whose risks were either significantly or nominally\nincreased by AI-IR and performed a combined analysis. The combined analysis further\nconfirmed the link between insulin resistance and cancer. This is mentioned in page 12, line\n200 in the revised manuscript.\n“When we defined composite cancers by merging 10 cancer types (common to both\nmales and females) whose risks were either significantly increased (kidney,\nesophagus, pancreas, colon) or nominally increased (renal pelvis, small intestine,\nstomach, liver and GB, colon, and bronchial and lung) by AI-IR, a Kaplan-Meier plot\nrevealed a significant difference between participants with diabetes, those without\ndiabetes but positive for AI-IR, and those without diabetes and negative for AI-IR, on\nthe cumulative incidence of the composite cancers (Fig. 4a). The HR adjusted for age\nand sex in participants without diabetes but positive for AI-IR was 1.25 (95% CI, 1.20-\n1.31; P < 1 x 10-11), and that in participants with diabetes was 1.40 (95% CI, 1.31-\n1.50; P < 1 x 10-11).”\n2. The paper’s use of a machine learning approach for predicting insulin resistance could be\nstrengthened by comparisons to simpler, established metrics, such as the TG/HDL ratio or\nTG-glucose index, to evaluate whether the model offers significant advantages over these\nalternatives. Additionally, while the authors mention in lines 67-68 that HOMA-IR is\nconsidered the best alternative to the hyperinsulinemic-euglycemic clamp, they do not\n27\nelaborate on the reasoning behind this assertion, leaving a gap in the justification for their\nmethodological choices.\nResponse\nin response to this constructive comment, we directly compared AI-IR and previously\nreported surrogate markers of insulin resistance such as TG/HDL ratio and TyG index\nregarding the predictive capabilities of the incidence of diabetes. We demonstrated that AI-\nIR is the best predictor of diabetes incidence compared to BMI, MetS, TG/HDL ratio, and TyG\nindex (page 9, line 134 in the revised manuscript).\n“To examine whether AI-IR outperforms previously established simpler metrics of\nobesity, metabolic syndrome, and insulin resistance, we compared the predictive\ncapabilities of AI-IR, BMI, metabolic syndrome (MetS), TG/HDL ratio, and TyG index\nfor the incidence of diabetes among individuals without diabetes at baseline during\nthe follow-up period. TG/HDL ratio24,25 and TyG index26–28 has been reported as a\nsurrogate markers of insulin resistance. Area under the curve (AUC) of the receiver\noperating characteristic (ROC) curve was highest for AI-IR (0.798), followed by MetS\n(0.748), BMI (0.721), TyG index (0.703), and Tg/HDL ratio (0.702), indicating the AI-\nIR exhibits the best predictive capability among these metrics (Fig. 2a).”\nWe also compared AI-IR with TG/HDL ratio and TyG inedx regarding the prediction of the\nincidence of the composite cancer (page 16, line 273 in the revised manuscript).\n“Finally, to examine whether AI-IR provides a better prediction of the composite cancer\nincidence than previously established metrics such as BMI, MetS, TG/HDL ratio, and\nTyG index, we classified participants who were free of diabetes and cancer at baseline\ninto four groups based on their AI-IR status and the status of these metrics.”\n(page 17, line 284 in the revised manuscript)\nWhen considering AI-IR and MetS, the AI-IR single positive group (HR, 1.21; 95% CI\n1.12-1.31; P = 2.1 x 10-6) and the MetS single positive group (HR, 1.19; 95% CI 1.12-\n1.26; P = 1.8 x 10-9) exhibited comparable HRs (P = 0.649) for the incidence of the\ncomposite cancers. However, the incidence of composite cancers was significantly\nhigher (P = 1.49 × 10⁻⁴) in the AI-IR and MetS double-positive group (HR, 1.34; 95%\nCI 1.27-1.40; P < 1 × 10⁻¹¹) compared to the MetS single-positive group (Fig. 5b).\nSimilar patterns were observed when we consider AI-IR and TG/HDL ratio (Fig. 5c).\nWhen considering AI-IR and TyG index, the incidence of the composite cancers was\nsignificantly higher (P = 1.92 x 10-2) for the AI-IR single-positive group (HR, 1.23; 95%\nCI, 1.13-1.35; P = 7.2 x 10-6) compared to the TyG single-positive group (HR, 1.10;\n95% CI, 1.05-1.16; P = 1.1 x 10-4). We also observed that the incidence of the\ncomposite cancers was significantly higher (P < 1 x 10-11) for the AI-IR and TyG\ndouble-positive group (HR, 1.32; 95% CI 1.26-1.39; P < 1 x 10-11) compared to the\nTyG single positive group (Fig. 5d). Altogether, these results indicate that AI-IR\nenables improved cancer risk stratification compared to previously established metrics.\nIn the introduction, in response to this reviewer comment, we additionally explained the\nreason that HOMA-IR can be considered as a best alternative to the hyperinsulinemic-\neuglycemic clamp (page 6, line 79 in the revised manuscript).\n“The homeostasis model assessment of insulin resistance (HOMA-IR) index, in which\nthe product of fasting plasma glucose (FPG) and fasting plasma insulin is divided by\na constant20, has been considered a best alternative method because HOMA-IR\ninvolves the direct measurement of insulin and glucose, and have been shown to\ncorrelate strongly with the gold standard clamp technique21,22.”\n29\n3. The study primarily focuses on statistical associations, without exploring the biological\nmechanisms that might link insulin resistance to cancer. A deeper examination of these\nunderlying mechanisms—such as insulin’s role in cellular proliferation or its involvement in\ninflammation pathways—could strengthen the paper by connecting the statistical findings to\nestablished aspects of cancer biology.\nResponse\nThe authors acknowledge that investigating the biological mechanisms linking insulin\nresistance to cancer is an important direction for future research. Dedicated studies would\nbe required to address this question in a thorough and meaningful way. Potential\nmechanisms include insulin’s role in cellular proliferation and obesity-induced inflammation.\nBy leveraging AI-IR, for example, we can conduct genome-wide association studies on\ninsulin resistance itself, rather than using BMI (a surrogate marker of insulin resistance) or\ntype 2 diabetes (a consequence of insulin resistance alongside impaired insulin secretion).\nWe added discussion in page 22, line 367 in the revised manuscript.\n“Third, the mechanistic link between insulin resistance and cancer remained elusive\nin the current study. However, leveraging AI-IR could provide deeper insights into the\nbiology of insulin resistance, for example, by enabling genome-wide association\nstudies on insulin resistance itself, rather than relying on BMI (a surrogate marker of\ninsulin resistance) or type 2 diabetes (a consequence of insulin resistance alongside\nimpaired insulin secretion).”\n4. While smoking is examined as an interaction effect in lung and bronchial cancers, it is not\ncomprehensively addressed across other cancer types, which may influence results for\ncancers strongly linked to smoking, especially esophageal cancer. Including additional\nstratification analyses or adjusting for smoking across other cancer subtypes could help\nreduce potential confounding effects and enhance the robustness of the findings.\nResponse\nIn response to this comment, we performed additional analyses examining the effect of\nsmoking along with AI-IR. First, we looked at the effect of AI-IR on the incidence of bronchial\nand lung cancer as well as composite cancers while adjusting for smoking status as well as\nage, sex, and BMI, which is mentioned in page 15, line 247 in the revised manuscript.\n“Lung cancer is strongly associated with smoking, the leading risk factor for the global\nburden of cancer31. We observed that the effect of AI-IR on the incidence of bronchial\n30\nand lung cancer is significant even when we adjusted also for smoking status (never\nsmoker, previous smoker, and current smoker); The same is true for the incidence of\nthe composite cancers (Supplementary Fig. 5a-b).”\nIn addition to bronchial and lung cancer, we examined the potential joint effect of AI-IR and\nsmoking status on the incidence of the composite cancers. We observed similar results as in\nthe case of bronchial and lung cancer, further supporting the robustness of this finding (page\n16, line 261 in the revised manuscript).\n“We observed similar interaction effect on the incidence of the composite cancers;\npredicted insulin resistance increased the incidence in never smoker group and\nprevious smoker group, but not in current smoker group (Supplementary Fig. 5d).\nNotably, the composite cancers include esophagus, pancreas, stomach, liver, and\nbronchial and lung cancers, all of which are known to have increased risk due to\nsmoking32.”\nLee CL., Hiraike Y., et al. NCOMMS-24-48120A\nReviewer #2 (Remarks to the Author):\nThank you to the authors for addressing my previous comments, and editing the manuscript.\nResponse\nThe authors would like to thank the reviewer for acknowledging our effort to improve our\nmanuscript in response to your previous comments. In light of the additional comments below,\nwe have further revised our manuscript to improve clarity and to better support our conclusion.\nRelated to the metabolic syndrome compared to the AI-IR to the metabolic syndrome to\npredict diabetes. As age is a component of their AI-IR score, and the risk of diabetes\nincreases with age, it is not surprising that adding age to other metabolic syndrome criteria\nincreases the risk of diabetes. The authors adjusted for age, sex and BMI in lines 113 to 115.\nDid the authors adjust for age when comparing the metabolic syndrome to the AI-IR, as\nmetabolic syndrome criteria and age are clinical parameters available to any clinician without\nthe need for a machine learning model to predict the risk of diabetes.\nResponse\nWe thank the reviewer for this request for clarification. In our comparison of AI-IR and\nmetabolic syndrome, we categorized individuals into four groups based on AI-IR status\n(positive or negative) and metabolic syndrome status (positive or negative) and assessed the\nincidence of diabetes (Figure 2c in the revised manuscript). We adjusted for age and sex\nwhen calculating the odds ratios. However, in the previous version of the manuscript, Figure\n2b-e was mistakenly labeled as showing “univariate” analyses. We have corrected this in the\nrevised manuscript. After adjustment for age and sex, the odds ratio for diabetes incidence\nwas significantly higher (P = 1.55 x 10-3) in AI-IR single positive group (OR, 6.85; 95%CI,\n4.35-10.80; P < 1.2 x 10-16) compared with metabolic syndrome single positive group (OR,\n3.14; 95%CI, 2,07-4.77; P < 6.8 x 10-8).\nthat the AI-IR model includes the metabolic syndrome parameters that are known to be better\npredictors than BMI alone. It is surprising however, that the authors found no association\nbetween obesity and the risk of diabetes - do the authors have any explanation for this, as\nthe risk of diabetes is known to increase with increasing obesity.\nResponse\nWe thank the reviewer for this important comment. In our comparison of AI-IR and BMI,\nindividuals were categorized into four groups based on AI-IR status (positive or negative) and\nBMI status (≥ 30 or < 30) (Figure 2b in the revised manuscript). Thus, “BMI single positive\ngroup” represents individuals who are obese (BMI ≥ 30) but AI-IR negative. Given the\nparameters used to define AI-IR, these individuals are likely to have lower FPG, HbA1c, TG\nand higher HDL levels, which may correspond to a “metabolically healthy obesity”. We have\nclarified this point in page 10, line 153 in the revised manuscript.\n“When we categorized individuals into four groups based on AI-IR status (positive or\nnegative) and BMI (≥ 30 or < 30), we observed that the incidence of diabetes was\nsignificantly higher (P = 2.52 x 10-5) in AI-IR single-positive group (OR, 6.14; 95% CI,\n4.42-8.54; P < 1 x 10-20; adjusted for age and sex) compared with BMI single-positive\ngroup (OR, 0.86; 95% CI, 0.35-2.12; P = 0.74). Of note, BMI of 30 or higher alone did\nnot significantly increase diabetes incidence when individuals were negative for AI-IR,\nsuggesting that these subjects may represent “metabolically healthy” obese\nindividuals.”\nWhat is the rationale for the \"double positive\" groups - BMI is part of the AI-IR model, so\nadding BMI in again on top of that is presumably artificially inflating the association. Could\nthe authors clarify exactly what \"single positive\" and \"double positive\" means related to BMI\nand metabolic syndrome. It means presence or absence of metabolic syndrome and obesity\nas defined by BMI?\nResponse\nWe appreciate the reviewer for this constructive comment. In our comparison of AI-IR and\nbased on AI-IR status (positive or negative) and BMI status (≥ 30 or < 30). Accordingly,\n“double positive group” refers to individuals who are both obese (BMI ≥ 30) and AI-IR positive.\n“AI-IR single positive group” represent those who are AI-IR positive but not obese (BMI < 30),\nand “BMI single positive group” represent those who are obese (BMI ≥ 30) but AI-IR negative.\nDefining groups in this way does not artificially inflate the associations. We clarified the\ndefinition of four groups in the revised manuscript.\n(page 10, line 153 in the revised manuscript).\n“When we categorized individuals into four groups based on AI-IR status (positive or\nnegative) and BMI (≥ 30 or < 30),”\n(page 10, line 159 in the revised manuscript).\n“In contrast, diabetes incidence was markedly higher (P = 9.78 x 10-7) in AI-IR and\nBMI double-positive group (OR, 8.08; 95% CI 6.13-10.66; P < 1 x 10-20) compared\nwith BMI single positive group, underscoring that AI-IR provides substantially\nimproved diabetes risk stratification among obese (BMI ≥ 30) individuals (Fig. 2b).”\nThe criteria used to define the metabolic syndrome seem to be missing from the methods.\nResponse\nWe would like to appreciate this reviewer for evaluating our manuscript really carefully to\nimprove our manuscript. In response to this comment, we have added the definition of\nmetabolic syndrome in the Method section (page 27, line 462 in the revised manuscript).\n“Metabolic syndrome (MetS) was defined as the presence of at least three of the\nfollowing: (1) abdominal obesity (waist circumference ≥ 102 cm in male or ≥ 88 cm in\nfemale), (2) elevated triglycerides (≥ 150 mg/dL), (3) reduced HDL cholesterol (< 40\nmg/dL in male or < 50 mg/dL in female), (4) elevated blood pressure (≥130/85 mmHg),\nand (5) elevated fasting glucose (≥100 mg/dL). Use of medications for triglycerides,\nHDL cholesterol, blood pressure, or glucose was not considered, as medication use\nwas not included in the computation of AI-IR.”\nIn the discussion the authors argue that the AI-IR model would alter screening for diabetes,\ncardiovascular disease and cancer. Could the authors elaborate on what screening\nguidelines would be changed? The study includes individuals from the UK Biobank between\n40 and 69 years old - it is unclear how would these findings would change the current\nscreening guidelines for diabetes and cancer in individuals in this age group.\nWe agree with the reviewer that the present study alone is not sufficient to warrant changes\nin existing screening guidelines for diabetes or cancer in individuals aged 40- 69 years. Our\nfindings, however, demonstrated that AI-IR provides improved risk stratification compared\nwith established clinical metrics such as BMI, metabolic syndrome, TG/HDL and TyG index.\nThis suggests that, in the future, AI-IR could support identification of high-risk individuals who\nmay benefit from earlier or more intensive monitoring. We have revised the Discussion\nsection to clarify this point (page 22, line 379 in the revised manuscript).\n“Individuals identified as AI-IR positive could undergo earlier or more frequent\nmonitoring of glucose metabolism, cardiovascular risk factors, and age-appropriate\ncancer screening, thereby improving opportunities for early detection and intervention.”\nThe last sentence of the discussion says that the ML approach could help gain insight into\ndisease pathophysiology. It is not clear how their AI-IR approach provides any new insight\ninto disease pathophysiology.\nResponse\nWe appreciate the reviewer’s comment. As noted in the Discussion, we think that\nleveraging AI-IR could provide deeper insights into biology of insulin resistance, for example\nby enabling GWAS of insulin resistance. In the revised manuscript, in response to this\nreviewer comment, we modified the text to replace “biology of insulin resistance” with\n“pathophysiology of insulin resistance” to better align with the last sentence of the discussion.\n(page 23, line 389 in the revised manuscript)\n“However, leveraging AI-IR could provide deeper insights into the pathophysiology of\ninsulin resistance, for example, by enabling genome-wide association studies on\ninsulin resistance itself, rather than relying on BMI (a surrogate marker of insulin\nresistance) or type 2 diabetes (a consequence of insulin resistance alongside impaired\ninsulin secretion).”\n(page 23, line 395 in the revised manuscript)\n“Our model exhibited the highest predictive capability for the incidence of diabetes\nand composite cancers among the examined metrics, highlighting the potential of a\nmachine learning-assisted approach to enhance clinical practice and gain insights into\ndisease pathophysiology.”\nThe authors have been largely responsive to the concerns raised in my initial review.\nResponse\nThe authors sincerely thank this reviewer for positive evaluation of our manuscript and for\nacknowledging the improvements made in response to previous comments.\nReviewer #1\nThank you for asking me to review this paper. It is a very interesting paper with highly\npertinent results. I do have a few comments for the authors:\nResponse\nThe authors sincerely thank this reviewer for positively evaluating our manuscript, as well\nas for the constructive and valuable comments that help us further improve our manuscript.\nIn response to the specific comments below, we performed additional analyses, including\nCox proportional hazards models using age as the underlying time scale and stratified\nanalyses based on fasting duration prior to blood collection. We also extensively revised the\nMethod section to enhance clarity, reproducibility, and transparency. Overall, we believe that\nall the concerns raised by the reviewer have been thoroughly addressed.\nI agree with the previous reviewer, the models should have age as the underlying time\nvariable. Adjusting and stratifying by age at recruitment is not sufficient to fully account for\nage in the models. To ensure your models are robust, you should re-run them with age as\nthe underlying time variable.\nResponse\nThe authors would like to thank the reviewer for this really important comment. To account\nfor the strong effect of age on cancer incidence, we re-ran the Cox proportional hazards\nmodels using age as the underlying time scale. This approach ensures that each participant\ncontributes person-time from their age at enrollment until their age at censoring or event,\nproviding a more effective control for confounding by age than including age as a covariate.\nWe observed AI-IR remained a robust predictor of cancer incidence under this model. This\nis mentioned in page 14, line 238 in the revised manuscript.\n“To more rigorously account for confounding by age, we also conducted analyses with\nage as the underlying time variable. Significant differences in cancer incidence\npersisted among participants with diabetes, those without diabetes but AI-IR positive,\nand those without diabetes and AI-IR negative (Supplementary Fig. 6a.) The HR\nadjusted for sex and BMI in participants without diabetes but AI-IR positive was 1.16\n(95% CI, 1.10-1.22; P < 3.2 x 10-8), and that in participants with diabetes was 1.29\n(95% CI, 1.20-1.39; P < 1 x 10-10). The effect of AI-IR on 3-point MACE was also\nconsistent in these models (Supplementary Fig. 6b). Together, these findings\ndemonstrate the robustness of AI-IR in predicting complications of insulin resistance\n6\nwhile fully accounting for the effect of age.”\nUKB does not have fasting glucose available, fasting samples were not collected. You have\nnot addressed this in your paper, how could this affect the prediction of IR given the original\nmodel was training on fasting glucose levels? This should be addressed.\nResponse\nWe thank the reviewer for this really constructive comment. To address the concern\nregarding fasting status, we performed stratified analyses based on fasting duration prior to\nblood collection, which is described in page 7, line 107 in the revised manuscript.\n“Notably, although AI-IR was originally developed using fasting blood test results,\nblood samples in the UK Biobank were not consistently collected in the fasted state.\nTo address this, we stratified participants into three groups based on fasting duration\nprior to blood collection (less than 4 hours, 4 or more and less than 8 hours, 8 hours\nor more) and examined the association between AI-IR and the incidence of diabetes.\nAcross all categories, AI-IR positive participants consistently showed a significantly\nhigher risk of developing diabetes, supporting the robustness of our model\n(Supplementary Fig. 1a-c). Hereafter, to maintain maximum sample size, we did not\n7\nstratify or exclude participants based on fasting duration.”\nYou have undertaken a impressing amount of analysis. However, this makes the methods\nvery difficult to follow, and I'm not sure that your work would be replicable with the current\nmethods section. I think you should consider rewriting this section so it is easy to follow and\nunderstand exactly what was undertaken in each analysis. Additionally, I am not convinced\nthat the correct censor date has been applied to the non-cancer participants. The two data\nfields listed for applying censor date are country of birth data fields, so I cannot see how you\nused these. Further, all first cancer diagnoses are present in the cancer registry, so absence\nindicates no cancer diagnosis, so the censor date of the cancer registry should be applied to\nall participants, regardless of if they were diagnosed with a cancer or not.\nResponse\nWe thank this reviewer for this constructive comment. In response, we have extensively\nrevised the Method section to improve clarity, reproducibility, and transparency of our\nanalyses.\nRegarding the censor date for non-cancer participants, we have also extensively revised\nthe description and provided additional explanation (page 25, line 423 in the revised\nmanuscript).\n“Follow-up was conducted until the last available date in the registries: 31 December\n2020 for England, 30 November 2021 for Scotland, and 31 December 2016 for Wales.\nParticipants without cancer were censored at the last available date of the respective\n8\nregistry, ensuring consistent follow-up for all participants. For participants recorded in\nthe combined “Originating from England/Wales (E/W)” registry, it was not possible to\nassign registry-specific censor dates to each participant. In these cases, censor dates\nwere assigned as 31 December 2016 for participants born in Wales (determined by\nData-Fields 1647 and 20115), and 31 December 2020 for all others.”\nWe acknowledge that, for participants in the combined E/W registry, registry-specific\ncensor dates cannot be assigned with certainty, which represents a limitation of the available\nregistry data. As clarified by UK Biobank, Welsh cancer registry data had ceased to be\nincluded in NHS England cancer registry extracts from 2017 onwards, and the censoring\ndate for Welsh records has been revised accordingly.\n(https://biobank.ndph.ox.ac.uk/ukb/exinfo.cgi?src=Data_providers_and_dates)\nHow model adjustments were applied is not clear - I was under the understanding from the\nmethods that all models were adjusted for age, sex and BMI, however the section from line\n232 suggests that is not the case.\nResponse\nWe thank the reviewer for this request for clarification. Analyses were performed\ndepending on the context: as crude (unadjusted), adjusted for age and sex, or adjusted for\nage, sex, and BMI, to account for potential confounding. We have carefully reviewed the\nentire manuscript and confirmed that model adjustments are now clearly described in the\nmain text, figure legends, and captions.\nDid you consider co-linearity between the various measures investigated, given that all these\nvariables are included in the AI-IR model?\nResponse\nWe thank the reviewer for this insightful comment. In traditional regression models, co-\nlinearity between predictors can bias coefficient estimates and inflate standard errors, which\nis a major concern. However, in machine learning models like XGboost (which we used for\nAI-IR), the algorithm can naturally handle correlations among input features by modeling non-\nlinear interactions and making splits based on feature importance rather than relying on a\nsingle coefficient per variable. Therefore, correlations among input parameters do not affect\nthe predictive performance of AI-IR.\n9\nline141 - Can you test to see if there is a significant difference between AUCs?\nResponse\nWe thank the reviewer for this suggestion. We found that AI-IR had a significantly higher\nAUC compared to other metrics (page 9, line 146 in the revised manuscript).\n“Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was\nhighest for AI-IR (0.798, P < 1 x 10-4 vs other metrics), followed by MetS (0.748),\nBMI (0.721), TyG index (0.703), and Tg/HDL ratio (0.702), indicating the AI-IR exhibits\nthe best predictive capability among these metrics (Fig. 2a).”\nlines 142 - 144: it is really not clear what has been done here\nResponse\nIn response to this reviewer comment, we have revised the text to clarify our approach in\nFigure 2b-e. (page 10, line 149 in the revised manuscript).\n“Next, we categorized individuals without diabetes at baseline into four groups\naccording to both AI-IR status (positive or negative) and their status on previously\nestablished metrics (positive or negative).”\nFor example, when using BMI, individuals were categorized into four groups: (1) “double\nnegative group” (AI-IR negative and BMI < 30), which served as the reference group; (2) “AI-\nIR single positive group” (AI-IR positive and BMI < 30); (3) “BMI single positive group” (AI-IR\nnegative and BMI ≥ 30); (4) “double positive group” (AI-IR positive and BMI ≥ 30). This\nclassification scheme was applied analogously to other established metrics.\nline 148: given this contradicts previous research, can you be confident in your results? What\nmodels did you use to undertake this analysis?\nResponse\nWe thank the reviewer for this important comment. In our comparison of AI-IR and BMI\n10\n(Figure 2b in the revised manuscript), individuals were categorized into four groups based\non AI-IR status (positive or negative) and BMI status (≥ 30 or < 30). Thus, “BMI single positive\ngroup” represents individuals who are obese (BMI ≥ 30) but AI-IR negative. Given the\nparameters used to define AI-IR, these individuals are likely to have lower FPG, HbA1c, TG\nand higher HDL levels, which may correspond to a “metabolically healthy obesity”. We have\nclarified this point in page 10, line 153 in the revised manuscript.\n“When we categorized individuals into four groups based on AI-IR status (positive or\nnegative) and BMI (≥ 30 or < 30), we observed that the incidence of diabetes was\nsignificantly higher (P = 2.52 x 10-5) in AI-IR single-positive group (OR, 6.14; 95% CI,\n4.42-8.54; P < 1 x 10-20; adjusted for age and sex) compared with BMI single-positive\ngroup (OR, 0.86; 95% CI, 0.35-2.12; P = 0.74). Of note, BMI of 30 or higher alone did\nnot significantly increase diabetes incidence when individuals were negative for AI-IR,\nsuggesting that these subjects may represent “metabolically healthy” obese\nindividuals.”\nlines 153 - again, it's really unclear what you have done here, what are the 4 groups?\nResponse\nIn response to this comment, we clarified the explanation to define four groups for Figure\n2c (page 10, line 163 in the revised manuscript).\n“Similarly, when we categorized individuals without diabetes into four groups based\non both AI-IR status (positive or negative) and MetS (positive or negative)”\nIndividuals were categorized into four groups: (1) “double negative group” (AI-IR negative\nand MetS negative), which served as the reference group; (2) “AI-IR single positive group”\n(AI-IR positive and MetS negative); (3) “MetS single positive group” (AI-IR negative and MetS\npositive); (4) “double positive group” (AI-IR positive and MetS positive).\nlines 184-193: I think only Bonferroni sig results should be reported\nThe authors would like to thank the reviewer for this valuable comment. In the previous\nrevision, reviewer #6 (who has been replaced by reviewer #8 in the current round)\nrecommended that, to increase generalizability, we define “composite cancers” by combining\ncancer sites showing either significant or nominal associations with AI-IR. Following this\nsuggestion, we constructed the composite cancer outcome and performed pooled analyses.\nTherefore, we consider it important to report both significant and nominal associations—\nincluding those for renal pelvis, small intestine stomach, liver and GB, leukemia, and\nbronchial and lung—as they provide transparency and clarify how the composite cancer was\ndefined.\nlines 200 - 217: I'm not convinced this section adds to the paper, it feels like analysis for\nanalysis sake, rather than truly trying to understand the aetiology (which would be best done\nindividually for each cancer site)\nResponse\nWe thank the reviewer for this comment. Related to the previous point, in response to\nreviewer #6 in the previous round, we defined “composite cancers” to increase\ngeneralizability of our findings. We believe this composite approach adds value in two\nimportant ways: (1) it increases statistical power by pooling less-common cancers, thereby\nreducing the risk of false negatives; and (2) it provides a more clinically relevant and\ngeneralizable picture of the overall cancer burden associated with AI-IR, which would be\noverlooked if we only examined site-specific outcomes. While detailed investigation of\nindividual cancer sites is certainly important for understanding aetiology, the composite\ncancer analysis serves a different purpose—offering a broader assessment of overall cancer\nrisk associated with AI-IR.\nline 404: there is no non-UK area covered by UKB - can you clarify what you mean by this?\nResponse\nWe appreciate the reviewer’s request for clarification. The phrase “non-UK area” was\nintroduced in reference to country of birth (Data-Fields 1647 and 20115), which was\nmisleading since UK Biobank only includes participants residing in the UK. In the revised\nmanuscript, we corrected this point and extensively revised the Method section to clarify how\nthese data-fields were used when assigning censor dates for participants without cancer\n(page 25, line 423 in the revised manuscript).\n12\n“Follow-up was conducted until the last available date in the registries: 31 December\n2020 for England, 30 November 2021 for Scotland, and 31 December 2016 for Wales.\nParticipants without cancer were censored at the last available date of the respective\nregistry, ensuring consistent follow-up for all participants. For participants recorded in\nthe combined “Originating from England/Wales (E/W)” registry, it was not possible to\nassign registry-specific censor dates to each participant. In these cases, censor dates\nwere assigned as 31 December 2016 for participants born in Wales (determined by\nData-Fields 1647 and 20115), and 31 December 2020 for all others.”\nline 410: include ICD codes used.\nResponse\nIn response to this reviewer comment, we have added the ICD codes used to define\ndiabetes (page 25, line 434 in the revised manuscript).\n“while long-term follow-up relied on NHS-recorded diagnosis (ICD-10 E08, 09, 11, and\n13), with diagnosis date defined as the date of admission with diabetes.”\nReviewer #7 (Remarks on code availability):\nWhilst the code for the machine learning predicted insulin resistance is under patent, I do\nfeel like the code for this analysis should be provided (that is, the code for these analysis,\nwithout the code to generate the AI-IR values) so it is transparent exactly how the analysis\nwas undertaken.\nResponse\nWe thank the reviewer for this important comment. To ensure transparency while protecting\nour filed patent, we consulted our patent lawyer, who strongly advised against disclosing the\nsource code prior to the publication of the patent (please see the documents “250805_Case\nStatus_NAIPO.pdf” and “250805_Case Status_NAUPO.pdf” uploaded as a related\nmanuscript files). Consequently, the code for computing AI-IR from the nine input parameters\ncannot be publicly released at this time.\nTo provide transparency while respecting patent restrictions, we have created a website\nthat allows users to calculate AI-IR based on the nine input parameters. The current website\nis managed on a local server, but we plan to deploy it on a public cloud server to ensure\nbroader accessibility upon publication of this work.\nhttp://220.130.208.108:8000\nreplaces Reviewer #6\nAuthors seemed to have addressed Reviewer #6 comments.\nResponse\nThe authors sincerely thank the reviewer for acknowledging our efforts to address the\ncomments raised by Reviewer #6 and for recognizing the improvements made to our\nmanuscript.\nLee CL., Hiraike Y., et al. NCOMMS-24-48120B\nReviewer #2 (Remarks to the Author):\nEDITORIAL NOTE: This referee was not able to submit their report on this occasion. However,\nReviewer #3 assessed the response. While they consider that most points were addressed,\nthey noted that, when comparing between BMI-high and AI-IR positive groups, there is still\nsome concern regarding the underlying issue that BMI may account for diabetes risk in a\nmanner that considerably overlaps with AI-IR prediction.\nResponse\nThe authors would like to thank the Reviewer #3 for evaluating our response and for\npositively acknowledging our efforts to address the points raised in the previous review.\nRegarding the potential overlap between BMI and AI-IR in terms of diabetes prediction, in\nresponse to the EDITORIAL NOTE, we added discussion, which is mentioned in page 19,\nline 327 in the revised manuscript.\n“We acknowledge that BMI is the most influential single parameter for AI-IR. When we\ncategorized individuals into four groups based on AI-IR status (positive or negative)\nand BMI (≥ 30 or < 30) (Fig. 2b), BMI may contribute to diabetes risk in a manner that\npartially overlaps with AI-IR. Nevertheless, AI-IR significantly outperformed BMI and\nother metrics in predicting diabetes incidence, consistent with the notion that AI-IR\ncaptures multidimensional clinical and biochemical abnormalities beyond BMI alone.”\nReviewer #7 (Remarks to the Author):\nThank you for resubmitting this manuscript. You have addressed all my previous concerns.\nResponse\nThe authors sincerely thank the reviewer for acknowledging our efforts to address the\nprevious concerns.\n1"}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thank you to the authors for addressing my previous comments, and editing the manuscript.\nRelated to the metabolic syndrome compared to the AI-IR to the metabolic syndrome to predict diabetes. As age is a component of their AI-IR score, and the risk of diabetes increases with age, it is not surprising that adding age to other metabolic syndrome criteria increases the risk of diabetes. The authors adjusted for age, sex and BMI in lines 113 to 115.\nDid the authors adjust for age when comparing the metabolic syndrome to the AI-IR, as metabolic syndrome criteria and age are clinical parameters available to any clinician without the need for a machine learning model to predict the risk of diabetes.\nLines 145 - that the AI-IR model is better than BMI to predict diabetes is not surprising, given that the AI-IR model includes the metabolic syndrome parameters that are known to be better predictors than BMI alone.\nIt is surprising however, that the authors found no association between obesity and the risk of diabetes - do the authors have any explanation for this, as the risk of diabetes is known to increase with increasing obesity.\nWhat is the rationale for the \"double positive\" groups - BMI is part of the AI-IR model, so adding BMI in again on top of that is presumably artificially inflating the association. Could the authors clarify exactly what \"single positive\" and \"double positive\" means related to BMI and metabolic syndrome. It means presence or absence of metabolic syndrome and obesity as defined by BMI?\nThe criteria used to define the metabolic syndrome seem to be missing from the methods.\nIn the discussion the authors argue that the AI-IR model would alter screening for diabetes, cardiovascular disease and cancer. Could the authors elaborate on what screening guidelines would be changed? The study includes individuals from the UK Biobank between 40 and 69 years old - it is unclear how would these findings would change the current screening guidelines for diabetes and cancer in individuals in this age group.\nThe last sentence of the discussion says that the ML approach could help gain insight into disease pathophysiology. It is not clear how their AI-IR approach provides any new insight into disease pathophysiology.\n(Remarks on code availability)", "reviewer_3": "The authors have been largely responsive to the concerns raised in my initial review.\n(Remarks on code availability)", "reviewer_7": "Thank you for asking me to review this paper. It is a very interesting paper with highly pertinent results.\nI do have a few comments for the authors:\nI agree with the previous reviewer, the models should have age as the underlying time variable. Adjusting and stratifying by age at recruitment is not sufficient to fully account for age in the models. To ensure your models are robust, you should re-run them with age as the underlying time variable.\nUKB does not have fasting glucose available, fasting samples were not collected. You have not addressed this in your paper, how could this affect the prediction of IR given the original model was training on fasting glucose levels? This should be addressed.\nYou have undertaken a impressing amount of analysis. However, this makes the methods very difficult to follow, and I'm not sure that your work would be replicable with the current methods section. I think you should consider rewriting this section so it is easy to follow and understand exactly what was undertaken in each analysis. Additionally, I am not convinced that the correct censor date has been applied to the non-cancer participants. The the two data fields listed for applying censor date are country of birth data fields, so I cannot see how you used these. Further, all first cancer diagnoses are present in the cancer registry, so absence indicates no cancer diagnosis, so the censor date of the cancer registry should be applied to all participants, regardless of if they were diagnosed with a cancer or not.\nHow model adjustments were applied is not clear - I was under the understanding from the methods that all models were adjusted for age, sex and BMI, however the section from line 232 suggests that is not the case.\nDid you consider co-linearity between the various measures investigated, given that all these variables are included in the AI-IR model?\nline141 - Can you test to see if there is a significant difference between AUCs?\nlines 142 - 144: it is really not clear what has been done here\nline 148: given this contradicts previous research, can you be confident in your results? What models did you use to undertake this analysis?\nlines 153 - again, it's really unclear what you have done here, what are the 4 groups?\nlines 184-193: I think only Bonferroni sig results should be reported\nlines 200 - 217: I'm not convinced this section adds to the paper, it feels like analysis for analysis sake, rather than truly trying to understand the aetiology (which would be best done individually for each cancer site)\nline 404: there is no non-UK area covered by UKB - can you clarify what you mean by this?\nline 410: include ICD codes used.\n(Remarks on code availability)\nWhilst the code for the machine learning predicted insulin resistance is under patent, I do feel like the code for this analysis should be provided (that is, the code for these analysis, without the code to generate the AI-IR values) so it is transparent exactly how the analysis was undertaken.", "reviewer_8": "Authors seemed to have addressed Reviewer #6 comments.\n(Remarks on code availability)"}, "author_response": "Point-by-point response to the editor and reviewers\nLee CL., Hiraike Y., et al. NCOMMS-24-48120A\nReviewer #2 (Remarks to the Author):\nThank you to the authors for addressing my previous comments, and editing the manuscript.\nResponse\nThe authors would like to thank the reviewer for acknowledging our effort to improve our\nmanuscript in response to your previous comments. In light of the additional comments below,\nwe have further revised our manuscript to improve clarity and to better support our conclusion.\nRelated to the metabolic syndrome compared to the AI-IR to the metabolic syndrome to\npredict diabetes. As age is a component of their AI-IR score, and the risk of diabetes\nincreases with age, it is not surprising that adding age to other metabolic syndrome criteria\nincreases the risk of diabetes. The authors adjusted for age, sex and BMI in lines 113 to 115.\nDid the authors adjust for age when comparing the metabolic syndrome to the AI-IR, as\nmetabolic syndrome criteria and age are clinical parameters available to any clinician without\nthe need for a machine learning model to predict the risk of diabetes.\nResponse\nWe thank the reviewer for this request for clarification. In our comparison of AI-IR and\nmetabolic syndrome, we categorized individuals into four groups based on AI-IR status\n(positive or negative) and metabolic syndrome status (positive or negative) and assessed the\nincidence of diabetes (Figure 2c in the revised manuscript). We adjusted for age and sex\nwhen calculating the odds ratios. However, in the previous version of the manuscript, Figure\n2b-e was mistakenly labeled as showing “univariate” analyses. We have corrected this in the\nrevised manuscript. After adjustment for age and sex, the odds ratio for diabetes incidence\nwas significantly higher (P = 1.55 x 10-3) in AI-IR single positive group (OR, 6.85; 95%CI,\n4.35-10.80; P < 1.2 x 10-16) compared with metabolic syndrome single positive group (OR,\n3.14; 95%CI, 2,07-4.77; P < 6.8 x 10-8).\nthat the AI-IR model includes the metabolic syndrome parameters that are known to be better\npredictors than BMI alone. It is surprising however, that the authors found no association\nbetween obesity and the risk of diabetes - do the authors have any explanation for this, as\nthe risk of diabetes is known to increase with increasing obesity.\nResponse\nWe thank the reviewer for this important comment. In our comparison of AI-IR and BMI,\nindividuals were categorized into four groups based on AI-IR status (positive or negative) and\nBMI status (≥ 30 or < 30) (Figure 2b in the revised manuscript). Thus, “BMI single positive\ngroup” represents individuals who are obese (BMI ≥ 30) but AI-IR negative. Given the\nparameters used to define AI-IR, these individuals are likely to have lower FPG, HbA1c, TG\nand higher HDL levels, which may correspond to a “metabolically healthy obesity”. We have\nclarified this point in page 10, line 153 in the revised manuscript.\n“When we categorized individuals into four groups based on AI-IR status (positive or\nnegative) and BMI (≥ 30 or < 30), we observed that the incidence of diabetes was\nsignificantly higher (P = 2.52 x 10-5) in AI-IR single-positive group (OR, 6.14; 95% CI,\n4.42-8.54; P < 1 x 10-20; adjusted for age and sex) compared with BMI single-positive\ngroup (OR, 0.86; 95% CI, 0.35-2.12; P = 0.74). Of note, BMI of 30 or higher alone did\nnot significantly increase diabetes incidence when individuals were negative for AI-IR,\nsuggesting that these subjects may represent “metabolically healthy” obese\nindividuals.”\nWhat is the rationale for the \"double positive\" groups - BMI is part of the AI-IR model, so\nadding BMI in again on top of that is presumably artificially inflating the association. Could\nthe authors clarify exactly what \"single positive\" and \"double positive\" means related to BMI\nand metabolic syndrome. It means presence or absence of metabolic syndrome and obesity\nas defined by BMI?\nResponse\nWe appreciate the reviewer for this constructive comment. In our comparison of AI-IR and\nbased on AI-IR status (positive or negative) and BMI status (≥ 30 or < 30). Accordingly,\n“double positive group” refers to individuals who are both obese (BMI ≥ 30) and AI-IR positive.\n“AI-IR single positive group” represent those who are AI-IR positive but not obese (BMI < 30),\nand “BMI single positive group” represent those who are obese (BMI ≥ 30) but AI-IR negative.\nDefining groups in this way does not artificially inflate the associations. We clarified the\ndefinition of four groups in the revised manuscript.\n(page 10, line 153 in the revised manuscript).\n“When we categorized individuals into four groups based on AI-IR status (positive or\nnegative) and BMI (≥ 30 or < 30),”\n(page 10, line 159 in the revised manuscript).\n“In contrast, diabetes incidence was markedly higher (P = 9.78 x 10-7) in AI-IR and\nBMI double-positive group (OR, 8.08; 95% CI 6.13-10.66; P < 1 x 10-20) compared\nwith BMI single positive group, underscoring that AI-IR provides substantially\nimproved diabetes risk stratification among obese (BMI ≥ 30) individuals (Fig. 2b).”\nThe criteria used to define the metabolic syndrome seem to be missing from the methods.\nResponse\nWe would like to appreciate this reviewer for evaluating our manuscript really carefully to\nimprove our manuscript. In response to this comment, we have added the definition of\nmetabolic syndrome in the Method section (page 27, line 462 in the revised manuscript).\n“Metabolic syndrome (MetS) was defined as the presence of at least three of the\nfollowing: (1) abdominal obesity (waist circumference ≥ 102 cm in male or ≥ 88 cm in\nfemale), (2) elevated triglycerides (≥ 150 mg/dL), (3) reduced HDL cholesterol (< 40\nmg/dL in male or < 50 mg/dL in female), (4) elevated blood pressure (≥130/85 mmHg),\nand (5) elevated fasting glucose (≥100 mg/dL). Use of medications for triglycerides,\nHDL cholesterol, blood pressure, or glucose was not considered, as medication use\nwas not included in the computation of AI-IR.”\nIn the discussion the authors argue that the AI-IR model would alter screening for diabetes,\ncardiovascular disease and cancer. Could the authors elaborate on what screening\nguidelines would be changed? The study includes individuals from the UK Biobank between\n40 and 69 years old - it is unclear how would these findings would change the current\nscreening guidelines for diabetes and cancer in individuals in this age group.\nWe agree with the reviewer that the present study alone is not sufficient to warrant changes\nin existing screening guidelines for diabetes or cancer in individuals aged 40- 69 years. Our\nfindings, however, demonstrated that AI-IR provides improved risk stratification compared\nwith established clinical metrics such as BMI, metabolic syndrome, TG/HDL and TyG index.\nThis suggests that, in the future, AI-IR could support identification of high-risk individuals who\nmay benefit from earlier or more intensive monitoring. We have revised the Discussion\nsection to clarify this point (page 22, line 379 in the revised manuscript).\n“Individuals identified as AI-IR positive could undergo earlier or more frequent\nmonitoring of glucose metabolism, cardiovascular risk factors, and age-appropriate\ncancer screening, thereby improving opportunities for early detection and intervention.”\nThe last sentence of the discussion says that the ML approach could help gain insight into\ndisease pathophysiology. It is not clear how their AI-IR approach provides any new insight\ninto disease pathophysiology.\nResponse\nWe appreciate the reviewer’s comment. As noted in the Discussion, we think that\nleveraging AI-IR could provide deeper insights into biology of insulin resistance, for example\nby enabling GWAS of insulin resistance. In the revised manuscript, in response to this\nreviewer comment, we modified the text to replace “biology of insulin resistance” with\n“pathophysiology of insulin resistance” to better align with the last sentence of the discussion.\n(page 23, line 389 in the revised manuscript)\n“However, leveraging AI-IR could provide deeper insights into the pathophysiology of\ninsulin resistance, for example, by enabling genome-wide association studies on\ninsulin resistance itself, rather than relying on BMI (a surrogate marker of insulin\nresistance) or type 2 diabetes (a consequence of insulin resistance alongside impaired\ninsulin secretion).”\n(page 23, line 395 in the revised manuscript)\n“Our model exhibited the highest predictive capability for the incidence of diabetes\nand composite cancers among the examined metrics, highlighting the potential of a\nmachine learning-assisted approach to enhance clinical practice and gain insights into\ndisease pathophysiology.”\nThe authors have been largely responsive to the concerns raised in my initial review.\nResponse\nThe authors sincerely thank this reviewer for positive evaluation of our manuscript and for\nacknowledging the improvements made in response to previous comments.\nReviewer #1\nThank you for asking me to review this paper. It is a very interesting paper with highly\npertinent results. I do have a few comments for the authors:\nResponse\nThe authors sincerely thank this reviewer for positively evaluating our manuscript, as well\nas for the constructive and valuable comments that help us further improve our manuscript.\nIn response to the specific comments below, we performed additional analyses, including\nCox proportional hazards models using age as the underlying time scale and stratified\nanalyses based on fasting duration prior to blood collection. We also extensively revised the\nMethod section to enhance clarity, reproducibility, and transparency. Overall, we believe that\nall the concerns raised by the reviewer have been thoroughly addressed.\nI agree with the previous reviewer, the models should have age as the underlying time\nvariable. Adjusting and stratifying by age at recruitment is not sufficient to fully account for\nage in the models. To ensure your models are robust, you should re-run them with age as\nthe underlying time variable.\nResponse\nThe authors would like to thank the reviewer for this really important comment. To account\nfor the strong effect of age on cancer incidence, we re-ran the Cox proportional hazards\nmodels using age as the underlying time scale. This approach ensures that each participant\ncontributes person-time from their age at enrollment until their age at censoring or event,\nproviding a more effective control for confounding by age than including age as a covariate.\nWe observed AI-IR remained a robust predictor of cancer incidence under this model. This\nis mentioned in page 14, line 238 in the revised manuscript.\n“To more rigorously account for confounding by age, we also conducted analyses with\nage as the underlying time variable. Significant differences in cancer incidence\npersisted among participants with diabetes, those without diabetes but AI-IR positive,\nand those without diabetes and AI-IR negative (Supplementary Fig. 6a.) The HR\nadjusted for sex and BMI in participants without diabetes but AI-IR positive was 1.16\n(95% CI, 1.10-1.22; P < 3.2 x 10-8), and that in participants with diabetes was 1.29\n(95% CI, 1.20-1.39; P < 1 x 10-10). The effect of AI-IR on 3-point MACE was also\nconsistent in these models (Supplementary Fig. 6b). Together, these findings\ndemonstrate the robustness of AI-IR in predicting complications of insulin resistance\n6\nwhile fully accounting for the effect of age.”\nUKB does not have fasting glucose available, fasting samples were not collected. You have\nnot addressed this in your paper, how could this affect the prediction of IR given the original\nmodel was training on fasting glucose levels? This should be addressed.\nResponse\nWe thank the reviewer for this really constructive comment. To address the concern\nregarding fasting status, we performed stratified analyses based on fasting duration prior to\nblood collection, which is described in page 7, line 107 in the revised manuscript.\n“Notably, although AI-IR was originally developed using fasting blood test results,\nblood samples in the UK Biobank were not consistently collected in the fasted state.\nTo address this, we stratified participants into three groups based on fasting duration\nprior to blood collection (less than 4 hours, 4 or more and less than 8 hours, 8 hours\nor more) and examined the association between AI-IR and the incidence of diabetes.\nAcross all categories, AI-IR positive participants consistently showed a significantly\nhigher risk of developing diabetes, supporting the robustness of our model\n(Supplementary Fig. 1a-c). Hereafter, to maintain maximum sample size, we did not\n7\nstratify or exclude participants based on fasting duration.”\nYou have undertaken a impressing amount of analysis. However, this makes the methods\nvery difficult to follow, and I'm not sure that your work would be replicable with the current\nmethods section. I think you should consider rewriting this section so it is easy to follow and\nunderstand exactly what was undertaken in each analysis. Additionally, I am not convinced\nthat the correct censor date has been applied to the non-cancer participants. The two data\nfields listed for applying censor date are country of birth data fields, so I cannot see how you\nused these. Further, all first cancer diagnoses are present in the cancer registry, so absence\nindicates no cancer diagnosis, so the censor date of the cancer registry should be applied to\nall participants, regardless of if they were diagnosed with a cancer or not.\nResponse\nWe thank this reviewer for this constructive comment. In response, we have extensively\nrevised the Method section to improve clarity, reproducibility, and transparency of our\nanalyses.\nRegarding the censor date for non-cancer participants, we have also extensively revised\nthe description and provided additional explanation (page 25, line 423 in the revised\nmanuscript).\n“Follow-up was conducted until the last available date in the registries: 31 December\n2020 for England, 30 November 2021 for Scotland, and 31 December 2016 for Wales.\nParticipants without cancer were censored at the last available date of the respective\nregistry, ensuring consistent follow-up for all participants. For participants recorded in\nthe combined “Originating from England/Wales (E/W)” registry, it was not possible to\nassign registry-specific censor dates to each participant. In these cases, censor dates\nwere assigned as 31 December 2016 for participants born in Wales (determined by\nData-Fields 1647 and 20115), and 31 December 2020 for all others.”\nWe acknowledge that, for participants in the combined E/W registry, registry-specific\ncensor dates cannot be assigned with certainty, which represents a limitation of the available\nregistry data. As clarified by UK Biobank, Welsh cancer registry data had ceased to be\nincluded in NHS England cancer registry extracts from 2017 onwards, and the censoring\ndate for Welsh records has been revised accordingly.\n(https://biobank.ndph.ox.ac.uk/ukb/exinfo.cgi?src=Data_providers_and_dates)\nHow model adjustments were applied is not clear - I was under the understanding from the\nmethods that all models were adjusted for age, sex and BMI, however the section from line\n232 suggests that is not the case.\nResponse\nWe thank the reviewer for this request for clarification. Analyses were performed\ndepending on the context: as crude (unadjusted), adjusted for age and sex, or adjusted for\nage, sex, and BMI, to account for potential confounding. We have carefully reviewed the\nentire manuscript and confirmed that model adjustments are now clearly described in the\nmain text, figure legends, and captions.\nDid you consider co-linearity between the various measures investigated, given that all these\nvariables are included in the AI-IR model?\nResponse\nWe thank the reviewer for this insightful comment. In traditional regression models, co-\nlinearity between predictors can bias coefficient estimates and inflate standard errors, which\nis a major concern. However, in machine learning models like XGboost (which we used for\nAI-IR), the algorithm can naturally handle correlations among input features by modeling non-\nlinear interactions and making splits based on feature importance rather than relying on a\nsingle coefficient per variable. Therefore, correlations among input parameters do not affect\nthe predictive performance of AI-IR.\n9\nline141 - Can you test to see if there is a significant difference between AUCs?\nResponse\nWe thank the reviewer for this suggestion. We found that AI-IR had a significantly higher\nAUC compared to other metrics (page 9, line 146 in the revised manuscript).\n“Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was\nhighest for AI-IR (0.798, P < 1 x 10-4 vs other metrics), followed by MetS (0.748),\nBMI (0.721), TyG index (0.703), and Tg/HDL ratio (0.702), indicating the AI-IR exhibits\nthe best predictive capability among these metrics (Fig. 2a).”\nlines 142 - 144: it is really not clear what has been done here\nResponse\nIn response to this reviewer comment, we have revised the text to clarify our approach in\nFigure 2b-e. (page 10, line 149 in the revised manuscript).\n“Next, we categorized individuals without diabetes at baseline into four groups\naccording to both AI-IR status (positive or negative) and their status on previously\nestablished metrics (positive or negative).”\nFor example, when using BMI, individuals were categorized into four groups: (1) “double\nnegative group” (AI-IR negative and BMI < 30), which served as the reference group; (2) “AI-\nIR single positive group” (AI-IR positive and BMI < 30); (3) “BMI single positive group” (AI-IR\nnegative and BMI ≥ 30); (4) “double positive group” (AI-IR positive and BMI ≥ 30). This\nclassification scheme was applied analogously to other established metrics.\nline 148: given this contradicts previous research, can you be confident in your results? What\nmodels did you use to undertake this analysis?\nResponse\nWe thank the reviewer for this important comment. In our comparison of AI-IR and BMI\n10\n(Figure 2b in the revised manuscript), individuals were categorized into four groups based\non AI-IR status (positive or negative) and BMI status (≥ 30 or < 30). Thus, “BMI single positive\ngroup” represents individuals who are obese (BMI ≥ 30) but AI-IR negative. Given the\nparameters used to define AI-IR, these individuals are likely to have lower FPG, HbA1c, TG\nand higher HDL levels, which may correspond to a “metabolically healthy obesity”. We have\nclarified this point in page 10, line 153 in the revised manuscript.\n“When we categorized individuals into four groups based on AI-IR status (positive or\nnegative) and BMI (≥ 30 or < 30), we observed that the incidence of diabetes was\nsignificantly higher (P = 2.52 x 10-5) in AI-IR single-positive group (OR, 6.14; 95% CI,\n4.42-8.54; P < 1 x 10-20; adjusted for age and sex) compared with BMI single-positive\ngroup (OR, 0.86; 95% CI, 0.35-2.12; P = 0.74). Of note, BMI of 30 or higher alone did\nnot significantly increase diabetes incidence when individuals were negative for AI-IR,\nsuggesting that these subjects may represent “metabolically healthy” obese\nindividuals.”\nlines 153 - again, it's really unclear what you have done here, what are the 4 groups?\nResponse\nIn response to this comment, we clarified the explanation to define four groups for Figure\n2c (page 10, line 163 in the revised manuscript).\n“Similarly, when we categorized individuals without diabetes into four groups based\non both AI-IR status (positive or negative) and MetS (positive or negative)”\nIndividuals were categorized into four groups: (1) “double negative group” (AI-IR negative\nand MetS negative), which served as the reference group; (2) “AI-IR single positive group”\n(AI-IR positive and MetS negative); (3) “MetS single positive group” (AI-IR negative and MetS\npositive); (4) “double positive group” (AI-IR positive and MetS positive).\nlines 184-193: I think only Bonferroni sig results should be reported\nThe authors would like to thank the reviewer for this valuable comment. In the previous\nrevision, reviewer #6 (who has been replaced by reviewer #8 in the current round)\nrecommended that, to increase generalizability, we define “composite cancers” by combining\ncancer sites showing either significant or nominal associations with AI-IR. Following this\nsuggestion, we constructed the composite cancer outcome and performed pooled analyses.\nTherefore, we consider it important to report both significant and nominal associations—\nincluding those for renal pelvis, small intestine stomach, liver and GB, leukemia, and\nbronchial and lung—as they provide transparency and clarify how the composite cancer was\ndefined.\nlines 200 - 217: I'm not convinced this section adds to the paper, it feels like analysis for\nanalysis sake, rather than truly trying to understand the aetiology (which would be best done\nindividually for each cancer site)\nResponse\nWe thank the reviewer for this comment. Related to the previous point, in response to\nreviewer #6 in the previous round, we defined “composite cancers” to increase\ngeneralizability of our findings. We believe this composite approach adds value in two\nimportant ways: (1) it increases statistical power by pooling less-common cancers, thereby\nreducing the risk of false negatives; and (2) it provides a more clinically relevant and\ngeneralizable picture of the overall cancer burden associated with AI-IR, which would be\noverlooked if we only examined site-specific outcomes. While detailed investigation of\nindividual cancer sites is certainly important for understanding aetiology, the composite\ncancer analysis serves a different purpose—offering a broader assessment of overall cancer\nrisk associated with AI-IR.\nline 404: there is no non-UK area covered by UKB - can you clarify what you mean by this?\nResponse\nWe appreciate the reviewer’s request for clarification. The phrase “non-UK area” was\nintroduced in reference to country of birth (Data-Fields 1647 and 20115), which was\nmisleading since UK Biobank only includes participants residing in the UK. In the revised\nmanuscript, we corrected this point and extensively revised the Method section to clarify how\nthese data-fields were used when assigning censor dates for participants without cancer\n(page 25, line 423 in the revised manuscript).\n12\n“Follow-up was conducted until the last available date in the registries: 31 December\n2020 for England, 30 November 2021 for Scotland, and 31 December 2016 for Wales.\nParticipants without cancer were censored at the last available date of the respective\nregistry, ensuring consistent follow-up for all participants. For participants recorded in\nthe combined “Originating from England/Wales (E/W)” registry, it was not possible to\nassign registry-specific censor dates to each participant. In these cases, censor dates\nwere assigned as 31 December 2016 for participants born in Wales (determined by\nData-Fields 1647 and 20115), and 31 December 2020 for all others.”\nline 410: include ICD codes used.\nResponse\nIn response to this reviewer comment, we have added the ICD codes used to define\ndiabetes (page 25, line 434 in the revised manuscript).\n“while long-term follow-up relied on NHS-recorded diagnosis (ICD-10 E08, 09, 11, and\n13), with diagnosis date defined as the date of admission with diabetes.”\nReviewer #7 (Remarks on code availability):\nWhilst the code for the machine learning predicted insulin resistance is under patent, I do\nfeel like the code for this analysis should be provided (that is, the code for these analysis,\nwithout the code to generate the AI-IR values) so it is transparent exactly how the analysis\nwas undertaken.\nResponse\nWe thank the reviewer for this important comment. To ensure transparency while protecting\nour filed patent, we consulted our patent lawyer, who strongly advised against disclosing the\nsource code prior to the publication of the patent (please see the documents “250805_Case\nStatus_NAIPO.pdf” and “250805_Case Status_NAUPO.pdf” uploaded as a related\nmanuscript files). Consequently, the code for computing AI-IR from the nine input parameters\ncannot be publicly released at this time.\nTo provide transparency while respecting patent restrictions, we have created a website\nthat allows users to calculate AI-IR based on the nine input parameters. The current website\nis managed on a local server, but we plan to deploy it on a public cloud server to ensure\nbroader accessibility upon publication of this work.\nhttp://220.130.208.108:8000\nreplaces Reviewer #6\nAuthors seemed to have addressed Reviewer #6 comments.\nResponse\nThe authors sincerely thank the reviewer for acknowledging our efforts to address the\ncomments raised by Reviewer #6 and for recognizing the improvements made to our\nmanuscript.\nLee CL., Hiraike Y., et al. NCOMMS-24-48120B\nReviewer #2 (Remarks to the Author):\nEDITORIAL NOTE: This referee was not able to submit their report on this occasion. However,\nReviewer #3 assessed the response. While they consider that most points were addressed,\nthey noted that, when comparing between BMI-high and AI-IR positive groups, there is still\nsome concern regarding the underlying issue that BMI may account for diabetes risk in a\nmanner that considerably overlaps with AI-IR prediction.\nResponse\nThe authors would like to thank the Reviewer #3 for evaluating our response and for\npositively acknowledging our efforts to address the points raised in the previous review.\nRegarding the potential overlap between BMI and AI-IR in terms of diabetes prediction, in\nresponse to the EDITORIAL NOTE, we added discussion, which is mentioned in page 19,\nline 327 in the revised manuscript.\n“We acknowledge that BMI is the most influential single parameter for AI-IR. When we\ncategorized individuals into four groups based on AI-IR status (positive or negative)\nand BMI (≥ 30 or < 30) (Fig. 2b), BMI may contribute to diabetes risk in a manner that\npartially overlaps with AI-IR. Nevertheless, AI-IR significantly outperformed BMI and\nother metrics in predicting diabetes incidence, consistent with the notion that AI-IR\ncaptures multidimensional clinical and biochemical abnormalities beyond BMI alone.”\nReviewer #7 (Remarks to the Author):\nThank you for resubmitting this manuscript. You have addressed all my previous concerns.\nResponse\nThe authors sincerely thank the reviewer for acknowledging our efforts to address the\nprevious concerns.\n1"}, {"round": 3, "reviewer_comments": {"reviewer_7": "Thank you for resubmitting this manuscript. You have addressed all my previous concerns."}, "author_response": "Point-by-point response to the editor and reviewers\nLee CL., Hiraike Y., et al. NCOMMS-24-48120B\nReviewer #2 (Remarks to the Author):\nEDITORIAL NOTE: This referee was not able to submit their report on this occasion. However,\nReviewer #3 assessed the response. While they consider that most points were addressed,\nthey noted that, when comparing between BMI-high and AI-IR positive groups, there is still\nsome concern regarding the underlying issue that BMI may account for diabetes risk in a\nmanner that considerably overlaps with AI-IR prediction.\nResponse\nThe authors would like to thank the Reviewer #3 for evaluating our response and for\npositively acknowledging our efforts to address the points raised in the previous review.\nRegarding the potential overlap between BMI and AI-IR in terms of diabetes prediction, in\nresponse to the EDITORIAL NOTE, we added discussion, which is mentioned in page 19,\nline 327 in the revised manuscript.\n“We acknowledge that BMI is the most influential single parameter for AI-IR. When we\ncategorized individuals into four groups based on AI-IR status (positive or negative)\nand BMI (≥ 30 or < 30) (Fig. 2b), BMI may contribute to diabetes risk in a manner that\npartially overlaps with AI-IR. Nevertheless, AI-IR significantly outperformed BMI and\nother metrics in predicting diabetes incidence, consistent with the notion that AI-IR\ncaptures multidimensional clinical and biochemical abnormalities beyond BMI alone.”\nReviewer #7 (Remarks to the Author):\nThank you for resubmitting this manuscript. You have addressed all my previous concerns.\nResponse\nThe authors sincerely thank the reviewer for acknowledging our efforts to address the\nprevious concerns.\n1"}]

Decision letter