Glycaemic index, glycaemic load and cancer risk: results from the prospective NutriNet-Santé cohort

Dietary carbohydrate quality index and incidence of obesity-related cancers in the "Seguimiento Universidad De Navarra" (SUN) prospective cohort

PURPOSE

The quality, rather than the quantity, of carbohydrate intake may play a major role in the etiology of obesity-related cancers (ORCs). We assessed the association between a previously defined carbohydrate quality index (CQI) and the risk of developing ORCs in the "Seguimiento Universidad de Navarra" (SUN) cohort.

METHODS

A total of 18,446 Spanish university graduates [mean age 38 years (SD 12 years), 61% women, mean BMI 23.5 kg/m2 (SD 3.5 kg/m2)], with no personal history of cancer, were followed-up. Baseline CQI was assessed summing quintiles of four previously defined criteria: high dietary fiber intake, low glycemic index (GI), high whole-grain: total-grain carbohydrates ratio and high solid carbohydrates: total carbohydrates ratio. Participants were classified into tertiles of their total CQI. Incident ORCs were confirmed by an oncologist using medical records and by querying the National Death Index blindly to dietary exposures.

RESULTS

During a median follow-up of 13.7 years, 269 incident cases of ORC were confirmed. A higher CQI was inversely associated with ORC incidence [multivariable-adjusted hazard ratio (HR) for the upper (T3) versus the lowest tertile (T1) of 0.68 (95% CI: 0.47-0.96), p for trend = 0.047]. Particularly, higher dietary fiber intake was inversely associated with ORC, HRT3 vs. T1=0.57 (95% CI 0.37-0.88 p for trend = 0.013).

CONCLUSION

In this prospective Mediterranean cohort, an inverse association between a better global quality of carbohydrate intake and the risk of ORCs was found. Strategies for cancer prevention should promote a higher quality of carbohydrate intake.

Overall glycaemic index and dietary glycaemic load and all-cause and cause-specific mortality in women from the Mexican Teachers' Cohort

Previous studies have found direct associations between glycaemic index (GI) and glycaemic load (GL) with chronic diseases. However, this evidence has not been consistent in relation to mortality, and most data regarding this association come from high-income and low-carbohydrate-intake populations. The aim of this study was to evaluate the association between the overall GI and dietary GL and all-cause mortality, CVD and breast cancer mortality in Mexico. Participants from the Mexican Teachers' Cohort (MTC) study in 2006-2008 were followed for a median of 10 years. Overall GI and dietary GL were calculated from a validated FFQ. Deaths were identified by the cross-linkage of MTC participants with two national mortality registries. Cox proportional hazard models were used to estimate the impact of GI and GL on mortality. We identified 1198 deaths. Comparing the lowest and highest quintile, dietary GI and GL appeared to be marginally associated with all-cause mortality; GI, 1·12 (95 % CI: 0·93, 1·35); GL, 1·12 (95 % CI: 0·87, 1·44). Higher GI and GL were associated with increased risk of CVD mortality, GI, 1·30 (95 % CI: 0·82, 2·08); GL, 1·64 (95 % CI: 0·87, 3·07) and with greater risk of breast cancer mortality; GI, 2·13 (95 % CI: 1·12, 4·06); GL, 2·43 (95 % CI: 0·90, 6·59). It is necessary to continue the improvement of carbohydrate quality indicators to better guide consumer choices and to lead the Mexican population to limit excessive intake of low-quality carbohydrate foods.

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BACKGROUND

Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.

METHODS

We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.

RESULTS

Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.

CONCLUSION

Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

The effects of glycemic index on prostate cancer progression in a xenograft mouse model

BACKGROUND

Previously, we found low-carbohydrate diets slowed prostate cancer (PC) growth and increased survival vs. a Western diet in mice, by inhibiting the insulin/IGF-1 axis. Thus, we tested whether modifying carbohydrate quality to lower glycemic index (GI) without changing quantity results in similar benefits as with reduced quantity.

METHODS

Male SCID mice injected with LAPC-4 cells were single-housed and randomized when their tumors reached 200 mm3 on average to a LoGI (48% carbohydrate kcal, from Hylon-VII) or HiGI Western diet (48% carbohydrate kcal, from sucrose). Body weight and tumor volume were measured weekly. Body composition was assessed 35 days after randomization. Blood glucose and serum insulin, IGF-1 and IGFBP3 were measured at study end when tumor volumes reached 800 mm3. We analyzed gene expression of mice tumors by RNA-sequencing and human tumors using the Prostate Cancer Transcriptome Atlas.

RESULTS

There were no significant differences in tumor volume (P > 0.05), tumor proliferation (P = 0.29), and overall survival (P = 0.15) between groups. At 35 days after randomization, the LoGI group had 30% lower body fat (P = 0.007) despite similar body weight (P = 0.58). At sacrifice, LoGI mice had smaller livers (P < 0.001) and lower glucose (P = 0.15), insulin (P = 0.11), IGF-1 (P = 0.07) and IGF-1:IGFBP3 ratio (P = 0.05), and higher IGFBP3 (P = 0.09) vs. HiGI, although none of these metabolic differences reached statistical significance. We observed differential gene expression and pathway enrichment in mice tumors by diet. The most upregulated and downregulated gene in the LoGI group showed expression patterns more closely resembling expression in human benign prostate tissue vs. PC.

CONCLUSIONS

In this single mouse xenograft model, consuming a low GI diet did not delay PC growth or survival vs. a high GI diet despite suggestions of decreased activation of the insulin/IGF-1 pathway. These data suggest that improving carbohydrate quality alone while consuming a high carbohydrate diet may not effectively slow PC growth.

Dietary glycemic index, glycemic load and all-cause and cause-specific mortality: A meta-analysis of prospective cohort studies

BACKGROUND & AIMS

The findings of previous studies investigating the association between dietary glycemic index, glycemic load, and the risk of mortality have been inconsistent. We performed a meta-analysis to evaluate this association.

METHODS

A systematic search in PubMed and Web of Science databases was conducted to identify prospective cohort studies on dietary glycemic index and load with risk of mortality through January 2023. Study-specific relative risks (RR) were combined by using random effects models.

RESULTS

Fifteen prospective cohort studies with a total of 527,650 participants and 48,598 all-cause and cause-specific deaths were included in the current meta-analysis. Pooled analyses indicated a higher risk of all-cause mortality (RR = 1.10, 95% CI: 1.00-1.20) and stroke mortality (RR = 1.30, 95% CI: 1.04-1.62) for the highest versus lowest levels of glycemic index. A significant non-linear association was found between glycemic index and mortality of all-causes (P for non-linearity = 0.02) and CVD (P for non-linearity <0.001), indicating increased risk at high levels of glycemic index (≥63.1 for all-cause mortality; ≥72.8 for CVD mortality). Glycemic load was positively associated with risk of CVD mortality (RR = 1.18, 95% CI: 1.09-1.27) and stroke mortality (RR = 1.30, 95% CI: 1.05-1.60) in the highest versus lowest meta-analysis. For cancer mortality, there was no significant association with glycemic index, but the association with glycemic load differed by sex.

CONCLUSIONS

Our results indicated that high glycemic index and glycemic load was associated with an increased risk of mortality from CVD and stroke. Further large prospective studies are warranted to provide definitive evidence in subgroups.

Dietary Approach of Patients with Hormone-Related Cancer Based on the Glycemic Index and Glycemic Load Estimates

Hormone-related cancers, namely breast, endometrial, cervical, prostate, testicular, and thyroid, constitute a specific group of cancers dependent on hormone levels that play an essential role in cancer growth. In addition to the traditional risk factors, diet seems to be an important environmental factor that partially explains the steadily increased prevalence of this group of cancer. The composition of food, the dietary patterns, the endocrine-disrupting chemicals, and the way of food processing and preparation related to dietary advanced glycation end-product formation are all related to cancer. However, it remains unclear which specific dietary components mediate this relationship. Carbohydrates seem to be a risk factor for cancer in general and hormone-related cancers, in particular, with a difference between simple and complex carbohydrates. Glycemic index and glycemic load estimates reflect the effect of dietary carbohydrates on postprandial glucose concentrations. Several studies have investigated the relationship between the dietary glycemic index and glycemic load estimates with the natural course of cancer and, more specifically, hormone-related cancers. High glycemic index and glycemic load diets are associated with cancer development and worse prognosis, partially explained by the adverse effects on insulin metabolism, causing hyperinsulinemia and insulin resistance, and also by inflammation and oxidative stress induction. Herein, we review the existing data on the effect of diets focusing on the glycemic index and glycemic load estimates on hormone-related cancers.

Plant-based dietary patterns defined by a priori indices and colorectal cancer risk by sex and race/ethnicity: the Multiethnic Cohort Study

BACKGROUND

Plant-based diets assessed by a priori indices are associated with health outcomes. This study investigated the associations between pre-defined indices of plant-based diets and risk of colorectal cancer (CRC) and evaluated whether the association varies by sex, race and ethnicity, and anatomic subsite of tumors.

METHODS

A total of 79,952 men and 93,475 women who participated in the Multiethnic Cohort Study were included. Primary outcome was incidence of invasive CRC. Cox models were used to estimate the risk of CRC across quintiles of three plant-based diet scores: overall plant-based diet index (PDI), healthful plant-based diet index (hPDI), and unhealthful plant-based diet index (uPDI).

RESULTS

During a mean follow-up of 19.2 years, 4976 incident CRC were identified. Among men, multivariable-adjusted HR (95% CI) for the highest vs. lowest quintiles was 0.77 (0.67-0.88) for PDI, and 0.80 (0.70-0.91) for hPDI, while no significant association was found for uPDI among men and for all indices among women. In men, the inverse association for PDI was stronger in Japanese American, Native Hawaiian, and White groups than African American or Latino group (P for heterogeneity = 0.01) and for left colon and rectal tumors than right tumors (P for heterogeneity = 0.005), whereas the decreased risk with hPDI was found consistently across racial and ethnic groups and subsites.

CONCLUSIONS

Greater adherence to plant-based diets rich in healthy plant foods and low in less healthy plant foods is associated with a reduced risk of CRC in men, but not in women. The strength of the association among men may vary by race and ethnicity and anatomic subsite of tumors.

Multiomics Integrated Analysis Identifies SLC24A2 as a Potential Link between Type 2 Diabetes and Cancer

Background

So far, type 2 diabetes (T2D) is considered as an independent risk factor for various cancers, but the underlying mechanism remains unclear. Methods. SLC24A2 was first identified as a key gene strongly associated with fasting plasma glucose (FPG). Then, overlapped differentially expressed genes (DEGs) between T2D verse control and SLC24A2-high verse SLC24A2-low were extracted and imported into weighted correlation network analysis. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used for functional enrichment analysis of DEGs. Least absolute shrinkage and selection operator was utilized to build a T2D prediction model. Timer and K-M plotters were employed to find the expression and prognosis of SLC24A2 in pan cancer.

Results

Interestingly, both DEGs between T2D verse control and SLC24A2-high verse SLC24A2-low enriched in cancer-related pathways. Moreover, a total of 3719 overlapped DEGs were divided into 8 functional modules. Grey module negatively correlated with T2D and FPG and was markedly involved in ribosome biogenesis. Ten SLC24A2-related genes (RRP36, RPF1, GRWD1, FBL, EXOSC5, BCCIP, UTP14A, TWISTNB, TBL3, and SKIV2L) were identified as hub genes, based on which the LASSO model accurately predicts the occurrence of T2D (AUC = 0.841). In addition, SLC24A2 was only expressed in islet β cells and showed abnormal expression in 17 kinds of cancers and significantly correlated with the prognosis of 10 kinds of cancers.

Conclusion

Taken together, SLC24A2 may link T2D and cancer by influencing the ribosome function of islet β cells and play different prognostic roles in different cancers.