Диабет мелитус в множестве миэломы и связанных с ним расстройства плазменных клеток: обзор риска, прогноза и результатов.

Introduction

Diabetes mellitus (DM) is a widely prevalent chronic health condition affecting approximately 463 million people (9.3%) worldwide, with type 2 contributing about 90% of the total cases [ 1 ]. Multiple myeloma (MM) is a plasma cell neoplasm that accounts for approximately 10% of all hematological malignancies [ 2 ]. Several studies suggest there is an increased incidence of cancer, mainly of the pancreas, liver, endometrium, breast, colorectum, bladder, and hematologic malignancies in individuals with type 2 DM [ 3 , 4 ]. The prevalence of DM in a retrospective study of 5383 MM patients was 15% (White, 12% and Black, 25%) [ 5 ]. Given the frequent co-occurrence of type 2 DM among MM patients, several studies have attempted to elucidate the potential link between these conditions [ 6 , 7 ]. This review summarizes the associations between DM and MM risk and outcomes, the impact of long-standing DM on the different clinical manifestations of MM, the various challenges faced by physicians in managing patients with coexistent DM and MM, and delves into the proposed mechanisms by which DM is postulated to increase cancer risk, especially MM risk.

Diabetes mellitus and time-dependent risk of MM

The relationship between DM and the time-dependent risk of MM was extensively examined in several studies, as seen in Table 1 and the impact of DM on MM outcomes and mortality is presented in Table 2 [ 4 – 13 ].

Notably, a comprehensive population-based study conducted in Israel [ 4 ] involving 408,243 persons with incident diabetes highlighted an increased risk of overall cancer including MM (1016 MM cases) following a DM diagnosis, with the highest risk observed within the first year after DM diagnosis [hazard ratio (HR) for MM 7.13; 95% confidence interval (95%CI) 6.01–8.44 in women and HR 6.72; 95%CI 5.75–7.86 in men], gradually decreasing but persisting for 11 years [ 4 ]. Similarly, a Canadian study analyzing 3,009,828 individuals reported a significantly higher risk of hematologic malignancies, particularly MM, in individuals with DM (adjusted HR 1.15; 95% CI 1.09–1.20), with the strongest associations observed among women and those aged under 65 [ 13 ].

Furthermore, a large population-based matched case-control study in Sweden assessed the risk of nine lymphoproliferative disorders as well as unspecified amyloidosis after a DM diagnosis in about 94,579 cases. The study found an initial increase in lymphoproliferative disorders within the first six months. More than six months post-diagnosis, DM remained associated with an elevated risk of specific malignancies, including acute lymphoblastic leukemia (ALL), chronic lymphoblastic leukemia (CLL), monoclonal gammopathy of undetermined significance (MGUS) (OR 1.33; 95%CI 1.25–1.41), and amyloidosis (OR: 1.62; 95% CI 1.44–1.83) [ 14 ]. However, sensitivity analysis controlling for the number of clinic visits demonstrated that risk for MGUS was not increased after DM diagnosis (OR 0.99; 95% CI 0.92–1.07), suggesting that the previously observed associations could be due to a detection bias [ 14 ]. This phenomenon reflects a time-dependent risk, which is the elevated incidence of MM shortly after DM diagnosis, which may be due to overlapping symptoms or increased medical surveillance leading to earlier detection, rather than a true causal relationship. As discussed in the study, this risk tends to decrease over time, especially with better DM control although still present.

Recent data also support an association between type 2 DM and MGUS. A large population-based study from Taiwan evaluated over 3.1 million individuals with incident type 2 DM and reported a significantly increased risk of developing MGUS (adjusted HR 2.33, 95% CI 1.93–2.81) [ 15 ]. The study proposed that metabolic dysfunction, chronic inflammation, and insulin resistance may contribute to this association, although age at DM onset appeared to modify the risk. Complementing these epidemiologic findings, emerging interventional data suggest that targeting metabolic pathways may influence MGUS biology. A recent phase II study evaluating metformin in patients with low-risk MGUS reported favorable progression-free outcomes, supporting a potential role for metabolic and inflammatory modulation in early plasma cell disorders [ 16 ].

However, in other studies, while there is a consistent trend in the same direction, this link was not statistically significant. The European Prospective Investigation into Cancer and Nutrition (EPIC) cohort study, involving 393,477 participants, did not find a significantly increased risk of MM [1,213 non-Hodgkin’s lymphoma and MM cases] among patients with self-reported DM (HR 1.09; 95% CI 0.80–1.47) [ 7 ]. Similarly, a meta-analysis of 10 observational studies done among patients with MM found no significant association between type 2 DM and MM incidence (OR 1.22; 95% CI 0.98–1.53) [ 6 ]. A systematic review and meta-analysis including 13 articles initially found a higher risk of MM among type 2 DM patients (OR: 1.60; 95% CI 1.13–2.26), but after accounting for heterogeneity and excluding 2 studies with high heterogeneity, the final analysis demonstrated no significant association between type 2 DM and incidence of MM (OR: 1.05; 95% CI 0.83–1.33) [ 17 ].

While interpreting these results, it is possible that these associations are influenced by co-existing obesity in DM patients. Individuals with an elevated BMI are significantly more likely to have DM suggesting that obesity is one of the strongest risk factors for DM [ 18 ]. There is also robust data to support an association between obesity and risk for MGUS, transformation of MGUS to MM, risk for development of MM, and increased MM mortality [ 19 , 20 ] and in fact, obesity has been identified as a major modifiable risk factor for MM [ 21 ]. Thus, obesity may represent a major confounding factor in evaluating the relationship between DM and MM. However, analyses in most of the studies described above were adjusted for BMI [ 6 , 8 , 11 , 12 ].

Thus, overall, while some studies suggest that pre-diagnosis DM increases the risk for MM in a time-dependent manner, with the greatest risk immediately after DM diagnosis others suggest this risk is not present.

Effect of DM on MM outcomes and mortality

A screening study of 35,420 men and women (66 MM deaths) identified a statistically significant 3-fold higher risk of MM mortality among women with the highest level (> 200 mg/dL) of post-load glucose, defined as blood glucose measured one hour after administration of 50 g oral glucose load to nondiabetic participants without regard to fasting status, (multivariable adjusted HR: 3.06, 95% CI 1.05–8.93) [ 10 ]. Another study evaluating cancer risk and mortality among patients with type 1 and type 2 DM enrolled in a national diabetes registry in Australia revealed significantly elevated standardized mortality ratios (SMRs) for all cancers in type 1 diabetic males (SMR: 1.19; 95% CI: 1.07–1.33) and females (SMR: 1.32; 95% CI: 1.17–1.49), as well as in type 2 diabetic males (SMR: 1.03; 95% CI: 1.01–1.04) and females (SMR: 1.13; 95% CI: 1.11–1.15) [ 12 ]. However, for multiple myeloma specifically, SMRs were not significantly elevated—type 1 diabetic males had an SMR of 1.17 (95% CI: 0.49–2.82), no MM deaths were reported in type 1 diabetic females, while type 2 diabetic males and females had SMRs of 0.94 (95% CI: 0.83–1.05) and 1.03 (95% CI: 0.87–1.20) respectively [ 12 ]. A single-center study involving 310 patients newly diagnosed with MM between 1999 and 2007 in Taiwan demonstrated that MM patients with pre-existing DM had a significantly higher proportion of renal impairment (serum creatinine ≥ 2 mg/dL), International Staging System (ISS) stage III disease and all-cause mortality; lower rates of reversal of renal impairment and lower overall survival (OS) (11.7 vs. 22.4 months; HR 1.5; 95% CI 1.02–2.23) when compared to non-diabetic MM patients [ 22 ].

Another study of 1,083 MM patients revealed that diabetic (history of type 2 DM or steroid-induced diabetes) patients had a statistically significant decreased probability of achieving complete remission after induction therapy and reduced survival (median OS: 65.4 vs. 98.7 months) compared with non-diabetic patients [ 23 ]. In a retrospective analysis of 5383 MM patients from two US academic institutions (15% with DM), patients with DM had a reduced overall survival (HR 1.27; 95% CI 1.11, 1.47; P < 0.001), especially among white patients [ 5 ]. These findings emphasize the importance of DM management in improving MM survival.

In contrast, a small study evaluating the impact of comorbidities such as DM on median PFS and OS in 114 nondiabetics and 13 diabetics found no significant difference on univariate analysis [ 24 ].

This data suggests that DM negatively impacts MM survival as well as increases complications such as renal disease. These findings highlight the importance of clinicians emphasizing adequate glycemic control through medications or dietary modifications.

DM and MM involve various organ systems

Additionally, although the association between DM and MM risk differs by study, DM is associated with multiple complications that increase morbidity and mortality and may overlap with those observed in MM. Some of the major organ systems affected by DM include neurologic, cardiovascular, renal, and ophthalmic, all of which can also be involved in MM.

Neuropathy

Peripheral neuropathy (PN) has been associated with plasma cell disorders [ 25 ] and is also a common complication seen in DM [ 26 ]. Additionally, therapies like bortezomib and thalidomide used in MM are associated with PN [ 27 ]. There is some evidence to suggest that patients with a history of DM have a higher risk of bortezomib neurotoxicity [ 28 , 29 ]. However, the phase III APEX trial, randomizing patients with relapsed MM to bortezomib vs high-dose dexamethasone, showed that the incidence and severity of PN appeared to be unaffected by baseline glycosylated hemoglobin level or diabetes history [ 30 ]. PN can lead to various complications, such as falls and fractures, and reduce the quality of life in patients [ 31 , 32 ]. It is, therefore, essential to assess patients for PN before starting chemotherapy and closely monitor their symptoms after initiating therapy.

Nephropathy

Renal failure is a significant adverse prognostic factor in patients with plasma cell dyscrasias [ 33 ]. Monoclonal gammopathy of renal significance (MGRS) is known to cause glomerular diseases and tubulopathies, and can involve the vasculature with varying presentations in patients [ 34 ]. Renal insufficiency has been identified at initial diagnosis in 15–50% of MM patients [ 35 ]. A study revealed that patients with renal failure had a lower response rate to chemotherapy than those with normal renal function (39% vs. 56%; p < 0.01). This difference may be partly attributed to dose adjustments necessitated by renal dysfunction. Moreover, patients who experienced recovery of renal function had a prolonged median survival than those without (28 months vs. 4 months; p < 0.001) [ 36 ]. Notably, MM patients who experience recovery of renal function continue to have inferior outcomes compared with those with normal renal function at diagnosis. In a large study ( N = 1135 newly diagnosed MM patients), those who presented with creatinine clearance (CrCl) < 40 mL/min at diagnosis and remained <40 mL/min and those with CrCl < 40 mL/min at diagnosis but subsequently improved to ≥ 40 mL/min had markedly shorter median overall survival (33 and 56 months respectively) compared with patients who had normal renal function at diagnosis (112 months) ( p < 0.001), highlighting that reversal of renal impairment does not fully mitigate its adverse prognostic impact [ 37 ]. Diabetic nephropathy occurs in 20–40% of all diabetics [ 38 ]. DM was the strongest independent factor that worsened renal prognosis in patients aged ≥ 65 years with monoclonal immunoglobulin deposition diseases, including MM (HR 3.65; 95% CI 2.08–6.41) [ 33 ].

Ophthalmic Complications

Although rare, ophthalmic manifestations of MM are diverse, ranging from proptosis, diplopia, scleritis, episcleritis, secondary glaucoma, uveal plasmacytoma, hyperviscosity leading to retinal vessel dilation, retinopathy, macular detachment, and macular edema [ 39 ]. DM-induced retinopathy may further add to the ophthalmic side effects of MM therapy, such as cataracts from prolonged steroid use [ 40 , 41 ] and keratopathy from belantamab mafodotin, an antibody-drug conjugate targeting B-cell maturation antigen, which has been associated with corneal epithelial damage through its cytotoxic payload, leading to microcyst-like epithelial changes [ 42 ]. Additionally, bortezomib has been associated with ocular toxicities such as blurred vision, chalazion, conjunctivitis, and, in rare cases, reversible optic neuropathy, likely due to mitochondrial dysfunction in retinal ganglion cells [ 43 ].

Cardiovascular Disease

DM is a well-known risk factor for cardiovascular disease. Individuals with DM, particularly type 2 DM, have higher cardiovascular morbidity and mortality compared with non-diabetic subjects [ 44 , 45 ]. Vascular complications of type 1 and type 2 DM., are major contributors to the development of coronary artery disease (CAD), stroke, and heart failure in affected individuals [ 46 ]. Severe anemia in MM can also worsen cardiac complication risk. AL amyloidosis can directly lead to cardiac complications such as heart failure and cardiomyopathy. AL amyloidosis can have cardiac involvement in about 70–80% of cases [ 47 ]. Several agents used in the treatment of MM, such as anthracyclines and proteasome inhibitors like carfilzomib, have been associated with cardiotoxicity [ 48 ]. Prolonged use of steroids is associated with the development of DM and hypertension, both of which are major risk factors for cardiovascular disease. Therefore, DM coexistent with MM can worsen cardiovascular risk.

Immune system

Individuals with DM are known to have defects in both innate and adaptive immune responses which contribute to their increased risk for infections. Additionally, obesity is often seen concomitantly with DM and may further amplify this immune dysregulation and inflammation. Supporting this metabolic–immune link, Gaucher disease provides a clear example of how chronic inflammation, lysolipid accumulation, and insulin resistance can drive plasma-cell dysregulation. In Gaucher disease, the clonal immunoglobulin frequently targets lipid antigens such as lyso-glucosylceramide, and remarkably, about one-third of sporadic MGUS and MM cases also show reactivity to these lysolipids [ 49 ]. This suggests that persistent lipid-driven immune activation may contribute to early plasmacytic transformation. Given that type 2 DM and obesity also feature lysolipid abnormalities, NF-κB activation, and chronic inflammation, similar antigenic mechanisms may influence MGUS/MM development in these populations.

Additionally, suppression of cytokine production (IL-2, IL-6, IL-10, IL-12, IL-17A, IL-22, interferons etc), defective leukocyte recruitment (due to lower expression of cellular adhesion molecules and other cytokines), defective pathogen recognition (due to down regulation of toll-like receptors), neutrophil, macrophage, monocyte and NK cell dysfunction and inhibition of antibody and complement pathways has been described in various studies [ 50 – 52 ].

Immune dysregulation is the hallmark of MM as it primarily affects plasma cells critical for antibody production and interferes with the production of other essential immune cells such as T-cells and neutrophils [ 53 , 54 ]. As a result, infections are a leading cause of morbidity and mortality in MM patients. A Latin American cohort study identified DM (OR 2.71, 95% CI 1.23–6.00) as a significant risk factor for infection-related hospitalization and mortality in newly diagnosed MM patients [ 55 ].

In addition, MM therapies, including proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and monoclonal antibodies (mAbs), significantly impact the immune system. PIs such as bortezomib can impair CD4 + T-cell function and have been associated with varicella-zoster virus reactivation [ 56 , 57 ]. CD38-directed monoclonal antibodies like daratumumab have been shown to reduce NK-cell counts, increasing infection risk [ 58 ]. In the context of immunotherapies, which rely heavily on intact immune surveillance, pre-existing immune dysfunction due to DM may further compromise efficacy and increase susceptibility to infections. There is data to suggest that diabetic patients with metastatic non-small cell lung cancer had a shorter PFS and OS when treated with pembrolizumab as compared to non-diabetic counterparts [ 59 ]. Similarly, as chimeric antigen receptor T-cell (CAR-T cell) therapy and bispecific antibodies emerge as the mainstay of treatment in relapsed, refractory multiple myeloma (RRMM) and possibly as part of upfront treatment for newly diagnosed multiple myeloma (NDMM) patients, the role of T-cell exhaustion as a mechanism of resistance to these therapies is being increasingly recognized [ 60 , 61 ]. Given the immune dysregulation occurring in DM patients, studies evaluating the impact of hyperglycemia and DM on outcomes of CAR-T cell and bispecific therapies are essential.

As such, individuals with DM may be at a disadvantage not only in mounting an adequate immune response to pathogens but also in recognizing and eliminating emerging tumor cells, which may contribute to MM pathogenesis and worsen outcomes in diabetic MM patients.

DM thus exacerbates MM complications across organ systems, including neurologic (PN), renal (nephropathy), ophthalmic, and cardiovascular manifestations, while also compromising immune function, potentially worsening MM outcomes.

Anti-hyperglycemic agents

Metformin use is associated with reduced overall cancer risk

Long-term use of metformin is associated with reduced incidence of cancer and cancer-related mortality [ 62 , 63 ]. A large meta-analysis of 265 studies (44 cohort studies and 39 case-control studies involving 7.6 million diabetic patients and 182 randomized controlled trials involving 137,540 diabetic patients) demonstrated reduced risk of cancer with metformin and thiazolidinediones (TZDs) (Relative Risk (RR) 0.86; 95%CI 0.83–0.90 and RR 0.93; 95% CI 0.91–0.96, respectively) [ 64 ]. The hypothesis that metformin synergizes with chemotherapeutic agents to increase chemosensitivity across a wide spectrum of malignancies has been evaluated in many case-control studies, meta-analyses, and is also currently being investigated prospectively in several clinical trials worldwide [ 62 ].

Metformin use is associated with reduced risk of transformation from MGUS to MM

Metformin use for ≥ 4 years was associated with a 53% lower risk of progression of MGUS to MM in a cohort of 2003 diabetic US veterans [ 65 ]. Based on this data, there is an ongoing clinical trial testing the effect of metformin on the prevention of progression in high-risk MGUS and low-risk SMM patients ( NCT04850846 ).

Anticancer mechanisms of metformin

Two widely accepted mechanisms are the inhibition of the AKT/mTOR signaling pathway with a consequent reduction in protein synthesis and suppression of cell proliferation [ 66 , 67 ] and reduction in circulating concentrations of insulin and insulin-like growth factor (IGF), thereby preventing activation of insulin and IGF receptor signaling pathways with resultant decreased growth promotion and mutagenesis [ 68 ]. Additionally, metformin has been shown to inhibit IL-6 receptor (IL-6R)–mediated signaling in MM cells through activation of AMPK, suppression of mTOR activity, and upregulation of the tumor-suppressor microRNA miR-34a, further supporting its potential antimyeloma effects [ 69 ]. Metformin was shown to suppress the induction of critical unfolded protein response effector glucose-regulated protein 78 (GRP78) with consequent impairment of autophagosome formation and enhancement of apoptosis when combined with bortezomib treatment in MM cell lines and patient tumor cells in-vitro [ 70 ]. These findings were also confirmed in-vivo in MM mice models in the same study.

Insulin increases cancer risk

In a study of 1303 Type 1 DM patients, insulin use was associated with increased cancer risk (incidence rate of 2.8 per 1000 person-years; 95%CI 2.2–3.3), especially with a higher insulin dose (HR 5.93; 95%CI 1.21–29.06), age (HR 1.08; 95%CI 1.05–1.12) and female sex (HR 1.74; 95%CI 1.15–2.64) [ 71 ].

Insulin may promote tumor growth both directly through insulin receptor activation and indirectly via upregulation of the insulin-like growth factor (IGF-1) signaling pathway [ 72 ]. Chronic hyperinsulinemia enhances hepatic production of IGF-1 and reduces IGF-1 binding proteins, thereby increasing bioavailability of IGF-1, which in turn activates IGF-1 receptors on malignant cells [ 73 ]. This axis is highly relevant to MM. IGF-1 has been shown to act as a potent myeloma growth factor at physiological concentrations [ 74 ]. Further supporting this, microarray analyses revealed significant overexpression of IGF-1 receptors in malignant plasma cells, indicating IGF’s signaling role in MM biology [ 75 ]. In a recent study, hyperinsulinemia was shown to accelerate MM progression as evidenced by increased tumor growth. In this study, MM.1S xenografts grew more rapidly in genetically engineered immunodeficient nonobese diabetic mice (Rag1−/−/MKR) compared to wild-type mice [ 5 ]. Western blot analysis revealed that these MM.1S xenografts from Rag1−/−/MKR mice had elevated levels of phosphorylated S6 ribosomal protein (Ser235/236) levels, indicating greater mTOR pathway activation [ 5 ]. This suggests a role for insulin signaling in promoting MM growth in vivo.

Additionally, a meta-regression analysis involving 21 case-control studies found that high circulating levels of IGF-1 are associated with increased cancer risk, including prostate (OR 1.49; 95% CI 1.14–1.95) and pre-menopausal breast cancer (OR 1.65, 95% CI 1.26–2.08), further highlighting the broad oncogenic potential of IGF signaling [ 76 ].

These findings collectively support the hypothesis that insulin and IGF-1 signaling may offer novel therapeutic strategies for MM prevention and treatment.

GLP-1 agonists are associated with reduced MM risk and reduced risk of transformation of MGUS to MM

Glucagon-like peptide 1 (GLP-1) agonists, recognized for their antidiabetic effects and cardiovascular benefits, have garnered attention for their potential role in slowing the progression of MGUS to MM in diabetic patients. Recent studies in US Veterans have shown a statistically significant 65% reduction in the risk of progression of MGUS to MM with GLP-1 agonist use (HR 0.35, 95% CI 0.14–0.90). Additionally, while GLP-1 agonist use demonstrated a remarkable 75% reduction in progression risk among white patients (HR 0.25; 95% CI 0.09–0.68), no significant decrease was observed in black patients (HR 1.01; 95% CI 0.36–2.90) [ 77 ]. In a large retrospective cohort study, GLP-1 agonist use was associated with a significantly lower incidence of MGUS (HR 0.68; 95% CI, 0.52–0.88) and MM (HR 0.49; 95% CI 0.31–0.76) compared to insulin use, whereas no significant difference was observed in incidence of MGUS (HR 1.23; 95%CI 0.93–1.63) or MM (HR 1.38; 95% CI 0.84–2.27) when compared to metformin use [ 78 ]. Similar findings were noted in another large nationwide retrospective cohort study of 1,651,452 US adults with type 2 DM, GLP-1 receptor agonist use was associated with a significantly lower risk of developing MM compared to insulin users (HR, 0.59; 95% CI, 0.44–0.77) [ 79 ]. These findings underscore the potential role of GLP-1 agonists in cancer prevention. Further prospective research trials are warranted to confirm these findings and further study underlying mechanisms.

Thus, metformin and GLP-1 agonists may potentially reduce the progression of MGUS to MM in diabetics via AKT/mTOR signaling modulation and impact on insulin/IGF pathway, while insulin use may be associated with an increased cancer risk. In addition to their metabolic effects, metformin and GLP-1 receptor agonists also improve the chronic inflammation and immune dysfunction seen in diabetes and obesity. Metformin reduces NF-κB activity and pro-inflammatory cytokines, while GLP-1 agonists have been shown to lower systemic inflammation and improve adipose-tissue inflammatory signaling [ 80 , 81 ]. These broader immunometabolic effects overlap with pathways involved in myeloma pathogenesis, raising the possibility that their favorable impact on MGUS and MM may in part relate to improving inflammation-driven immune dysregulation.

Pathways through which DM may cause carcinogenesis

Here, we outline the various mechanisms implicated in the increased cancer and MM risk associated with DM.

DM may contribute to carcinogenesis through three primary mechanisms: hormonal [insulin and insulin-like growth factor (IGF)], metabolic (hyperglycemia), and inflammatory [ 82 – 84 ] (Fig. 1 ). The association between the insulin/IGF axis and cancer progression has been extensively studied [ 76 ]. The association between insulin and cancer risk has been described above [ 71 ]. Hyperinsulinemia and insulin resistance in DM may also contribute to cancer development by activating the wingless-related integration site (Wnt) ligand, which either binds to an insulin receptor or increases circulating IGF levels [ 82 ]. Hyperinsulinemia also enhances hepatic IGF production [ 85 ] and exerts a pro-mitotic effect through insulin receptor-A (IR-A) [ 86 ]. MM cell lines have been shown to express IR-A and IGF receptors, which respond to IGFs by evading dexamethasone-induced apoptosis [ 87 ].

In addition to Wnt signaling, insulin and IGF also activate the phosphoinositide 3-kinase (PI3K)/AKT/ mTOR pathway- a central regulator of cell growth, proliferation, and survival [ 88 ]. Chronic activation of this pathway by hyperglycemia or increased IGF signaling contributes to carcinogenesis by promoting protein synthesis, inhibiting apoptosis, and enhancing glucose uptake in tumor cells [ 89 , 90 ]. Activation of PI3K /AKT signaling has been observed in MM, and this pathway may be further amplified in diabetic patients, providing a mechanistic link between metabolic dysregulation and plasma cell malignancy [ 91 , 92 ].

Hyperglycemia may increase carcinogenesis directly or indirectly. It can exert direct effects like the promotion of cell proliferation, inducing mutations, and influencing invasion, migration, and rewiring in cancer cells [ 83 ]. Indirectly, hyperglycemia may increase the circulation of insulin, IGF, and inflammatory cytokines, contributing to enhanced proliferation [ 83 ]. Interleukin-6 (IL-6), implicated in the development of various malignancies, including MM, is elevated in T2DM [ 93 , 94 ]. The pro-inflammatory state in uncontrolled DM, characterized by increased levels of IL-6, tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP), along with increased monocyte activity and NF-κB activation, may contribute to carcinogenesis [ 95 ]. This chronic inflammation leads to oxidative stress that damages lipids, proteins, and DNA, contributing to carcinogenesis [ 96 ]. Furthermore, TNF-α activates the nuclear factor-kappa B (NF-κB) pathway, which promotes angiogenesis, metastasis, and proliferation of malignant cells while impairing adaptive immunity [ 97 , 98 ]. MM cells have exhibited mutations in the NF-κB pathway, suggesting a role in MM development [ 99 , 100 ].

DM can thus foster carcinogenesis through hormonal, metabolic, and inflammatory pathways, involving mechanisms such as hyperinsulinemia, hyperglycemia-driven cell proliferation, and chronic inflammation. The combined activation of insulin/IGF axis signaling and downstream activation of PI3K /AKT/ mTOR and NF-κB pathways provides a plausible link between diabetes and increased cancer risk, including MM development.

Dietary modifications for DM and MM

In addressing the complex interplay between DM and MM, lifestyle modifications, particularly dietary interventions, emerge as crucial considerations. Lifestyle and nutritional habits significantly contribute to the rising incidence of DM, emphasizing the importance of improved nutritional practices in preventing complications of DM [ 101 , 102 ]. Prior randomized dietary studies testing a whole food plant-based diet (low in fat and high in complex carbohydrates rich in fiber) for 16 weeks have shown an improvement in hemoglobin A1c, insulin resistance, and diabetes [ 103 , 104 ]. This is consistent with expert consensus statements published and may help clinicians counsel patients on their diet post-diagnosis [ 105 , 106 ]. These findings are consistent with the NUTRIVENTION trial that enrolled participants with MGUS and SMM with an elevated body mass index, and demonstrated that a high-fiber plant-based diet not only improved insulin resistance and adiponectin-leptin ratio (a marker of insulin resistance), but also reduced systemic inflammatory biomarkers such as C-reactive protein and increased anti-inflammatory classical monocytes. The intervention additionally increased gut microbiota alpha-diversity and abundance of butyrate-producing bacteria, supporting interconnected metabolic, inflammatory, and immune pathways relevant to both DM biology and early myeloma progression [ 107 ]. A participant on the trial with DM on insulin and GLP-1 agonist experienced weight loss but M spike continued to progress until he went on the high fiber dietary intervention when he was able to stop insulin, and M spike plateaued [ 107 ]. However, further prospective trials are needed to understand the role of diet in addressing insulin resistance and DM in plasma cell disorders. The NUTRIVENTION-3 in MGUS/SMM is recruiting ( NCT05640843 ). The NUTRIVENTION-4 trial ( NCT04497961 ) in MM survivors is completed ( NCT04497961 ) and the decentralized NUTRIVENTION-5 trial is testing a high fiber diet in newly diagnosed MM is recruiting ( NCT07226609 ).

Conclusion

The association and interactions between DM and cancer, including MM, highlights the need for continued research. Additionally, lifestyle factors, particularly diet, warrant attention in managing DM patients. This review highlights the existing evidence and emphasizes the need for multidisciplinary care to navigate the care of these patients.

Acknowledgements

Not applicable.

Author contributions

Richa Parikh and Ram Prakash Thirugnanasambandam contributed equally to the conceptualization, literature review, and initial drafting of the manuscript. Both also contributed equally to the revisions and worked together in submitting the final version. Urvi Shah provided senior oversight, guided the structure and focus of the review, and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript.

Funding

This study was funded by the Center for Hematologic Malignancies at MSK and in part through the NIH/NCI Cancer Center Support Grant P30 CA008748. [U.A.S.]. U.A.S. is supported by the American Society of Hematology Scholar Award, Paula and Rodger Riney Foundation, Willow Foundation, HealthTree Foundation, Gabrielle’s Angel Foundation and Blood Cancer United.

Data availability

No data is available for this article as it is a review article.

Competing interests

R. Parikh reports no conflicts of interest. R.P. Thirugnanasambandam reports no conflict of interest. U.A. Shah reports research funding support from BMS and Janssen, to the institution, non-financial research support from Sabinsa Pharmaceuticals and M and M Labs to the institution; personal fees/honorarium from Janssen Biotech, and Sanofi, outside the submitted work.

Ethical approval

The study did not require IRB approval as it is a review article.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: R Parikh, RP Thirugnanasambandam.

References

1. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diab Res Clin Pr. 2019;157:107843.

2. Rajkumar SV, Kumar S. Multiple Myeloma: Diagnosis and Treatment. Mayo Clin Proc. 2016;91:101–19.

3. Noto H, Tsujimoto T, Noda M. Significantly increased risk of cancer in diabetes mellitus patients: A meta-analysis of epidemiological evidence in Asians and non-Asians. J Diab Investig. 2012;3:24–33.

4. Dankner R, Boffetta P, Balicer RD, Boker LK, Sadeh M, Berlin A, et al. Time-Dependent Risk of Cancer After a Diabetes Diagnosis in a Cohort of 2.3 Million Adults. Am J Epidemiol. 2016;183:1098–106.

5. Shah UA, Moshier E, Derkach A, Huang Y, Mailankody S, Tan CR, et al. Prevalence and impact of diabetes on survival of patients with multiple myeloma in different racial groups. Blood Adv. 2024;8:236–47.

6. Castillo JJ, Mull N, Reagan JL, Nemr S, Mitri J. Increased incidence of non-Hodgkin lymphoma, leukemia, and myeloma in patients with diabetes mellitus type 2: a meta-analysis of observational studies. Blood. 2012;119:4845–50.

7. Khan AE, Gallo V, Linseisen J, Kaaks R, Rohrmann S, Raaschou-Nielsen O, et al. Diabetes and the risk of non-Hodgkin’s lymphoma and multiple myeloma in the European Prospective Investigation into Cancer and Nutrition. Haematologica. 2008;93:842–50.

8. Vineis P, Crosignani P, Sacerdote C, Fontana A, Masala G, Miligi L, et al. Haematopoietic cancer and medical history: a multicentre case control study. J Epidemiol Community Health. 2000;54:431–6.

9. Fortuny J, Benavente Y, Bosch R, Garcia-Villanueva M, de Sevilla AF, de Sanjose S. Type 2 diabetes mellitus, its treatment and risk for lymphoma. Eur J Cancer. 2005;41:1782–7.

10. Chiu BC, Gapstur SM, Greenland P, Wang R, Dyer A. Body mass index, abnormal glucose metabolism, and mortality from hematopoietic cancer. Cancer Epidemiol Biomark Prev. 2006;15:2348–54.

11. Atchison EA, Gridley G, Carreon JD, Leitzmann MF, McGlynn KA. Risk of cancer in a large cohort of U.S. veterans with diabetes. Int J Cancer. 2011;128:635–43.

12. Harding JL, Shaw JE, Peeters A, Cartensen B, Magliano DJ. Cancer risk among people with type 1 and type 2 diabetes: disentangling true associations, detection bias, and reverse causation. Diab Care. 2015;38:264–70.

13. Gong IY, Cheung MC, Read S, Na Y, Lega IC, Lipscombe LL. Association between diabetes and haematological malignancies: a population-based study. Diabetologia. 2021;64:540–51.

14. Shah UA, Rognvaldsson S, Derkach A, Bjorkholm M, Turesson I, David Y, et al. Diabetes mellitus and risk of plasma cell and lymphoproliferative disorders in 94,579 cases and 368,348 matched controls. Haematologica. 2022;107:284–6.

15. Chang S-H, Wang M, Chen P-X, Hsu SH, Hu M-C, Wei C-C. Type 2 Diabetes Mellitus Increases the Risk for Developing Monoclonal Gammopathy of Undetermined Significance in Asian Populations. Blood. 2024;144:6898.

16. da Cunha A, Oliota A, Borsa M, Rizzotto ML, Ghirardi M, Parellada M, et al. Effect of Metformin on Progression-Free Survival in Patients with Monoclonal Gammopathy of Undetermined Significance (MGUS): A Phase II Interventional Study in Brazil. Clin Lymphoma, Myeloma Leuk. 2025;25:S237.

17. Zhang C, Sha Y, Liu H, Guo D, Jiang Y, Hong L, et al. Type 2 diabetes mellitus does not increase the risk of multiple myeloma: a systematic review and meta-analysis. Transl Cancer Res. 2020;9:2884–94.

18. Bays HE, Chapman RH, Grandy S. Group SI. The relationship of body mass index to diabetes mellitus, hypertension and dyslipidaemia: comparison of data from two national surveys. Int J Clin Pr. 2007;61:737–47.

19. Parikh R, Tariq SM, Marinac CR, Shah UA. A comprehensive review of the impact of obesity on plasma cell disorders. Leukemia. 2022;36:301–14.

20. Anuraj A, Rath D, Derkach A, Usmani SZ, Shah UA. Racial differences in the proportion of myeloma cases attributable to excess body weight and diabetes mellitus in the United States. Haematologica. 2026;111:703–6.

21. Marques-Mourlet C, Di Iorio R, Fairfield H, Reagan MR. Obesity and myeloma: Clinical and mechanistic contributions to disease progression. Front Endocrinol (Lausanne). 2023;14:1118691.

22. Chou YS, Yang CF, Chen HS, Yang SH, Yu YB, Hong YC, et al. Pre-existing diabetes mellitus in patients with multiple myeloma. Eur J Haematol. 2012;89:320–7.

23. Wu W, Merriman K, Nabaah A, Seval N, Seval D, Lin H, et al. The association of diabetes and anti-diabetic medications with clinical outcomes in multiple myeloma. Br J Cancer. 2014;111:628–36.

24. Kleber M, Ihorst G, Terhorst M, Koch B, Deschler B, Wasch R, et al. Comorbidity as a prognostic variable in multiple myeloma: comparative evaluation of common comorbidity scores and use of a novel MM-comorbidity score. Blood Cancer J. 2011;1:e35.

25. Rognvaldsson S, Steingrimsson V, Turesson I, Bjorkholm M, Landgren O, Yngvi Kristinsson S. Peripheral neuropathy and monoclonal gammopathy of undetermined significance: a population-based study including 15,351 cases and 58,619 matched controls. Haematologica. 2020;105:2679–81.

26. Head KA. Peripheral neuropathy: pathogenic mechanisms and alternative therapies. Alter Med Rev. 2006;11:294–329.

27. Chaudhry V, Cornblath DR, Polydefkis M, Ferguson A, Borrello I. Characteristics of bortezomib- and thalidomide-induced peripheral neuropathy. J Peripher Nerv Syst. 2008;13:275–82.

28. Badros A, Goloubeva O, Dalal JS, Can I, Thompson J, Rapoport AP, et al. Neurotoxicity of bortezomib therapy in multiple myeloma: a single-center experience and review of the literature. Cancer. 2007;110:1042–9.

29. Dimopoulos MA, Mateos MV, Richardson PG, Schlag R, Khuageva NK, Shpilberg O, et al. Risk factors for, and reversibility of, peripheral neuropathy associated with bortezomib-melphalan-prednisone in newly diagnosed patients with multiple myeloma: subanalysis of the phase 3 VISTA study. Eur J Haematol. 2011;86:23–31.

30. Richardson PG, Sonneveld P, Schuster MW, Stadtmauer EA, Facon T, Harousseau JL, et al. Reversibility of symptomatic peripheral neuropathy with bortezomib in the phase III APEX trial in relapsed multiple myeloma: impact of a dose-modification guideline. Br J Haematol. 2009;144:895–903.

31. Benbow SJ, Wallymahmed ME, MacFarlane IA. Diabetic peripheral neuropathy and quality of life. QJM. 1998;91:733–7.

32. Girach A, Julian TH, Varrassi G, Paladini A, Vadalouka A, Zis P. Quality of Life in Painful Peripheral Neuropathies: A Systematic Review. Pain Res Manag. 2019;2019:2091960.

33. Stratta P, Gravellone L, Cena T, Rossi D, Gaidano G, Fenoglio R, et al. Renal outcome and monoclonal immunoglobulin deposition disease in 289 old patients with blood cell dyscrasias: a single center experience. Crit Rev Oncol /Hematol. 2011;79:31–42.

34. Amaador K, Peeters H, Minnema MC, Nguyen TQ, Dendooven A, Vos JMI, et al. Monoclonal gammopathy of renal significance (MGRS) histopathologic classification, diagnostic workup, and therapeutic options. Neth J Med. 2019;77:243–54.

35. Korbet SM, Schwartz MM. Multiple myeloma. J Am Soc Nephrol. 2006;17:2533–45.

36. Blade J, Fernandez-Llama P, Bosch F, Montoliu J, Lens XM, Montoto S, et al. Renal failure in multiple myeloma: presenting features and predictors of outcome in 94 patients from a single institution. Arch Intern Med. 1998;158:1889–93.

37. Gonsalves WI, Leung N, Rajkumar SV, Dispenzieri A, Lacy MQ, Hayman SR, et al. Improvement in renal function and its impact on survival in patients with newly diagnosed multiple myeloma. Blood Cancer J. 2015;5:e296.

38. Gheith O, Farouk N, Nampoory N, Halim MA, Al-Otaibi T. Diabetic kidney disease: world wide difference of prevalence and risk factors. J Nephropharmacol. 2016;5:49–56.

39. Fung S, Selva D, Leibovitch I, Hsuan J, Crompton J. Ophthalmic manifestations of multiple myeloma. Ophthalmologica. 2005;219:43–8.

40. Banerjee R, Hurtado Martinez JA, Flores Perez PA, Porras N, Hydren J, Ahlstrom JM, et al. Association between dexamethasone exposure and visually significant cataracts in multiple myeloma. Am J Hematol. 2024;99:E12–E4.

41. Jobling AI, Augusteyn RC. What causes steroid cataracts? A review of steroid-induced posterior subcapsular cataracts. Clin Exp Optom. 2002;85:61–75.

42. Farooq AV, Degli Esposti S, Popat R, Thulasi P, Lonial S, Nooka AK, et al. Corneal Epithelial Findings in Patients with Multiple Myeloma Treated with Antibody-Drug Conjugate Belantamab Mafodotin in the Pivotal, Randomized, DREAMM-2 Study. Ophthalmol Ther. 2020;9:889–911.

43. Dennis M, Maoz A, Hughes D, Sanchorawala V, Sloan JM, Sarosiek S. Bortezomib ocular toxicities: Outcomes with ketotifen. Am J Hematol. 2019;94:E80–E2.

44. Schernthaner G. Cardiovascular mortality and morbidity in type-2 diabetes mellitus. Diab Res Clin Pr. 1996;31:S3–13.

45. Gu K, Cowie CC, Harris MI. Diabetes and decline in heart disease mortality in US adults. JAMA. 1999;281:1291–7.

46. Ahmed I, Goldstein BJ. Cardiovascular risk in the spectrum of type 2 diabetes mellitus. Mt Sinai J Med. 2006;73:759–68.

47. Sanchorawala V. Systemic Light Chain Amyloidosis. N Engl J Med. 2024;390:2295–307.

48. Mikhael J. Management of Carfilzomib-Associated Cardiac Adverse Events. Clin Lymphoma Myeloma Leuk. 2016;16:241–5.

49. Nair S, Branagan AR, Liu J, Boddupalli CS, Mistry PK, Dhodapkar MV. Clonal Immunoglobulin against Lysolipids in the Origin of Myeloma. N Engl J Med. 2016;374:555–61.

50. Holt RIG, Cockram CS, Ma RCW, Luk AOY. Diabetes and infection: review of the epidemiology, mechanisms and principles of treatment. Diabetologia. 2024;67:1168–80.

51. Rodacki M, Svoren B, Butty V, Besse W, Laffel L, Benoist C, et al. Altered natural killer cells in type 1 diabetic patients. Diabetes. 2007;56:177–85.

52. Odegaard JI, Chawla A. Connecting type 1 and type 2 diabetes through innate immunity. Cold Spring Harb Perspect Med. 2012;2:a007724.

53. Blimark C, Holmberg E, Mellqvist UH, Landgren O, Bjorkholm M, Hultcrantz M, et al. Multiple myeloma and infections: a population-based study on 9253 multiple myeloma patients. Haematologica. 2015;100:107–13.

54. Russell BM, Avigan DE. Immune dysregulation in multiple myeloma: the current and future role of cell-based immunotherapy. Int J Hematol. 2023;117:652–9.

55. Bove V, Riva E, Vasquez J, Pena C, Seehaus C, Samanez C, et al. Epidemiology and Risk Factors for the Development of Infectious Complications in Newly Diagnosed Multiple Myeloma: A Multicenter Prospective Cohort Study in Latin America. JCO Glob Oncol. 2022;8:e2200068.

56. Kim SJ, Kim K, Kim BS, Lee HJ, Kim H, Lee NR, et al. Bortezomib and the increased incidence of herpes zoster in patients with multiple myeloma. Clin Lymphoma Myeloma. 2008;8:237–40.

57. Heider U, Rademacher J, Kaiser M, Kleeberg L, von Metzler I, Sezer O. Decrease in CD4 + T-cell counts in patients with multiple myeloma treated with bortezomib. Clin Lymphoma Myeloma Leuk. 2010;10:134–7.

58. Nahi H, Chrobok M, Gran C, Lund J, Gruber A, Gahrton G, et al. Infectious complications and NK cell depletion following daratumumab treatment of Multiple Myeloma. PLoS One. 2019;14:e0211927.

59. Leshem Y, Dolev Y, Siegelmann-Danieli N, Sharman Moser S, Apter L, Chodick G, et al. Association between diabetes mellitus and reduced efficacy of pembrolizumab in non-small cell lung cancer. Cancer. 2023;129:2789–97.

60. Delgoffe GM, Xu C, Mackall CL, Green MR, Gottschalk S, Speiser DE, et al. The role of exhaustion in CAR T cell therapy. Cancer Cell. 2021;39:885–8.

61. Friedrich MJ, Neri P, Kehl N, Michel J, Steiger S, Kilian M, et al. The pre-existing T cell landscape determines the response to bispecific T cell engagers in multiple myeloma patients. Cancer Cell. 2023;41:711–25.e6.

62. Cunha Júnior AD, Pericole FV, Carvalheira JBC. Metformin and blood cancers. Clin (Sao Paulo, Braz). 2018;73:e412s.

63. Evans JM, Donnelly LA, Emslie-Smith AM, Alessi DR, Morris AD. Metformin and reduced risk of cancer in diabetic patients. BMJ. 2005;330:1304–5.

64. Wu L, Zhu J, Prokop LJ, Murad MH. Pharmacologic Therapy of Diabetes and Overall Cancer Risk and Mortality: A Meta-Analysis of 265 Studies. Sci Rep. 2015;5:10147.

65. Chang SH, Luo S, O’Brian KK, Thomas TS, Colditz GA, Carlsson NP, et al. Association between metformin use and progression of monoclonal gammopathy of undetermined significance to multiple myeloma in US veterans with diabetes mellitus: a population-based retrospective cohort study. Lancet Haematol. 2015;2:e30–6.

66. Zi FM, He JS, Li Y, Wu C, Yang L, Yang Y, et al. Metformin displays anti-myeloma activity and synergistic effect with dexamethasone in in vitro and in vivo xenograft models. Cancer Lett. 2015;356:443–53.

67. Gong J, Kelekar G, Shen J, Shen J, Kaur S, Mita M. The expanding role of metformin in cancer: an update on antitumor mechanisms and clinical development. Target Oncol. 2016;11:447–67.

68. Pollak M. The insulin and insulin-like growth factor receptor family in neoplasia: an update. Nat Rev Cancer. 2012;12:159–69.

69. Mishra AK, Dingli D. Metformin inhibits IL-6 signaling by decreasing IL-6R expression on multiple myeloma cells. Leukemia. 2019;33:2695–709.

70. Jagannathan S, Abdel-Malek MA, Malek E, Vad N, Latif T, Anderson KC, et al. Pharmacologic screens reveal metformin that suppresses GRP78-dependent autophagy to enhance the anti-myeloma effect of bortezomib. Leukemia. 2015;29:2184–91.

71. Zhong W, Mao Y. Daily Insulin Dose and Cancer Risk Among Patients With Type 1 Diabetes. JAMA Oncol. 2022;8:1356–8.

72. Gallagher EJ, LeRoith D. The proliferating role of insulin and insulin-like growth factors in cancer. Trends Endocrinol Metab. 2010;21:610–8.

73. Pollak M. Insulin and insulin-like growth factor signalling in neoplasia. Nat Rev Cancer. 2008;8:915–28.

74. Sprynski AC, Hose D, Kassambara A, Vincent L, Jourdan M, Rossi JF, et al. Insulin is a potent myeloma cell growth factor through insulin/IGF-1 hybrid receptor activation. Leukemia. 2010;24:1940–50.

75. De Vos J, Hose D, Reme T, Tarte K, Moreaux J, Mahtouk K, et al. Microarray-based understanding of normal and malignant plasma cells. Immunol Rev. 2006;210:86–104.

76. Renehan AG, Zwahlen M, Minder C, O’Dwyer ST, Shalet SM, Egger M. Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis. Lancet. 2004;363:1346–53.

77. Grandhi N, Liu L, Wang M, Thomas T, Schoen M, Sanfilippo K, et al. Association between glucagon-like peptide-1 receptor agonist use and progression of monoclonal gammopathy of uncertain significance to multiple myeloma among patients with diabetes. JNCI Cancer Spectr. 2024;8:pkae095.

78. Ashruf OS, Hundal J, Mushtaq A, Kaelber DC, Anwer F, Singh A. Hematologic Cancers Among Patients With Type 2 Diabetes Prescribed GLP-1 Receptor Agonists. JAMA Netw Open. 2025;8:e250802.

79. Wang L, Xu R, Kaelber DC, Berger NA. Glucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated Cancers in Patients With Type 2 Diabetes. JAMA Netw Open. 2024;7:e2421305.

80. Lin H, Ao H, Guo G, Liu M. The Role and Mechanism of Metformin in Inflammatory Diseases. J Inflamm Res. 2023;16:5545–64.

81. Lee YS, Jun HS. Anti-Inflammatory Effects of GLP-1-Based Therapies beyond Glucose Control. Mediators Inflamm. 2016;2016:3094642.

82. Garcia-Jimenez C, Garcia-Martinez JM, Chocarro-Calvo A, De la Vieja A. A new link between diabetes and cancer: enhanced WNT/beta-catenin signaling by high glucose. J Mol Endocrinol. 2014;52:R51–66.

83. Xu CX, Zhu HH, Zhu YM. Diabetes and cancer: Associations, mechanisms, and implications for medical practice. World J Diab. 2014;5:372–80.

84. Olatunde A, Nigam M, Singh RK, Panwar AS, Lasisi A, Alhumaydhi FA, et al. Cancer and diabetes: the interlinking metabolic pathways and repurposing actions of antidiabetic drugs. Cancer Cell Int. 2021;21:499.

85. Djiogue S, Nwabo Kamdje AH, Vecchio L, Kipanyula MJ, Farahna M, Aldebasi Y, et al. Insulin resistance and cancer: the role of insulin and IGFs. Endocr Relat Cancer. 2013;20:R1–R17.

86. Vella V, Milluzzo A, Scalisi NM, Vigneri P, Sciacca L. Insulin Receptor Isoforms in Cancer. Int J Mol Sci. 2018;19:3615.

87. Xu F, Gardner A, Tu Y, Michl P, Prager D, Lichtenstein A. Multiple myeloma cells are protected against dexamethasone-induced apoptosis by insulin-like growth factors. Br J Haematol. 1997;97:429–40.

88. Cantley LC. The phosphoinositide 3-kinase pathway. Science. 2002;296:1655–7.

89. Liu P, Cheng H, Roberts TM, Zhao JJ. Targeting the phosphoinositide 3-kinase pathway in cancer. Nat Rev Drug Discov. 2009;8:627–44.

90. Elstrom RL, Bauer DE, Buzzai M, Karnauskas R, Harris MH, Plas DR, et al. Akt stimulates aerobic glycolysis in cancer cells. Cancer Res. 2004;64:3892–9.

91. Huang X, Liu G, Guo J, Su Z. The PI3K/AKT pathway in obesity and type 2 diabetes. Int J Biol Sci. 2018;14:1483–96.

92. Wang L, Lin N, Li Y. The PI3K/AKT signaling pathway regulates ABCG2 expression and confers resistance to chemotherapy in human multiple myeloma. Oncol Rep. 2019;41:1678–90.

93. Chung TD, Yu JJ, Spiotto MT, Bartkowski M, Simons JW. Characterization of the role of IL-6 in the progression of prostate cancer. Prostate. 1999;38:199–207.

94. Feurino LW, Zhang Y, Bharadwaj U, Zhang R, Li F, Fisher WE, et al. IL-6 stimulates Th2 type cytokine secretion and upregulates VEGF and NRP-1 expression in pancreatic cancer cells. Cancer Biol Ther. 2007;6:1096–100.

95. Devaraj S, Dasu MR, Jialal I. Diabetes is a proinflammatory state: a translational perspective. Expert Rev Endocrinol Metab. 2010;5:19–28.

96. Reuter S, Gupta SC, Chaturvedi MM, Aggarwal BB. Oxidative stress, inflammation, and cancer: how are they linked?. Free Radic Biol Med. 2010;49:1603–16.

97. Hoesel B, Schmid JA. The complexity of NF-kappaB signaling in inflammation and cancer. Mol Cancer. 2013;12:86.

98. Karin M. NF-kappaB as a critical link between inflammation and cancer. Cold Spring Harb Perspect Biol. 2009;1:a000141.

99. Roy P, Sarkar UA, Basak S. The NF-kappaB Activating Pathways in Multiple Myeloma. Biomedicines. 2018;6:59.

100. Keats JJ, Fonseca R, Chesi M, Schop R, Baker A, Chng WJ, et al. Promiscuous mutations activate the noncanonical NF-kappaB pathway in multiple myeloma. Cancer Cell. 2007;12:131–44.

101. Ojo O. Dietary Intake and Type 2 Diabetes. Nutrients. 2019;11:2177.

102. Tan J, Liu N, Sun P, Tang Y, Qin W. A Proinflammatory Diet May Increase Mortality Risk in Patients with Diabetes Mellitus. Nutrients. 2022;14:2011.

103. Wright N, Wilson L, Smith M, Duncan B, McHugh P. The BROAD study: A randomised controlled trial using a whole food plant-based diet in the community for obesity, ischaemic heart disease or diabetes. Nutr Diab. 2017;7:e256.

104. Kahleova H, Petersen KF, Shulman GI, Alwarith J, Rembert E, Tura A, et al. Effect of a Low-Fat Vegan Diet on Body Weight, Insulin Sensitivity, Postprandial Metabolism, and Intramyocellular and Hepatocellular Lipid Levels in Overweight Adults: A Randomized Clinical Trial. JAMA Netw Open. 2020;3:e2025454.

105. Rosenfeld RM, Kelly JH, Agarwal M, Aspry K, Barnett T, Davis BC, et al. Dietary Interventions to Treat Type 2 Diabetes in Adults with a Goal of Remission: An Expert Consensus Statement from the American College of Lifestyle Medicine. Am J Lifestyle Med. 2022;16:342–62.

106. McMacken M, Shah S. A plant-based diet for the prevention and treatment of type 2 diabetes. J Geriatr Cardiol. 2017;14:342–54.

107. Shah UA, Cogrossi LL, Garces JJ, Policastro A, Castro F, Derkach A, et al. A High-Fiber Plant-Based Diet in Myeloma Precursor Disorders - Results from the NUTRIVENTION Clinical Trial and Preclinical Vk*MYC Model. Cancer Discov. 2026;16:697–720.

Associated Data

Data Availability Statement

No data is available for this article as it is a review article.