Dietary inflammatory index score, glucose control and cardiovascular risk factors profile in people with type 2 diabetes

Cross-sectional association among dietary habits, periodontitis, and uncontrolled diabetes in Hispanics: the LLIPDS study

Objectives

Type 2 Diabetes (T2D) is recognized as a risk factor for periodontal disease (PD), with evidence supporting a bidirectional relationship. Food choices are thought to influence both conditions, but research on their impact specifically on PD remains limited. This study aimed to explore whether food choices were linked to higher prevalence of adverse periodontal parameters and poorly controlled glucose levels among Hispanic adults with T2D.

Methods

A cross-sectional study was conducted with 260 Puerto Rican adults aged 40-65 years, all diagnosed with T2D. Dietary habits were assessed by weekly frequencies of food choices deemed healthy or unhealthy over the past year. Periodontal health was evaluated by the percentage of sites with probing pocket depth (PPD) ≥4 mm and bleeding on probing (BOP) at corresponding teeth. Glucose control was measured by glycated hemoglobin (HbA1c) levels, with uncontrolled glucose defined as HbA1c ≥ 7%. Linear regression models adjusted for demographic and clinical variables estimated associations with PD. Logistic regression assessed associations with glucose control.

Results

The median Healthy Eating Score was 0.5 (Q1, Q3: -3.9, 4.5). A higher Healthy Eating Score was significantly associated with fewer sites exhibiting PPD ≥ 4 mm and BOP (adjusted β: -0.02; SE: 0.01; p = 0.035), and reduced odds of uncontrolled glucose (adjusted odds ratio: 0.94; 95% CI: 0.89-0.98; p = 0.007).

Conclusions

Adherence to a healthier dietary pattern appears to correlate with lower periodontal inflammation and greater glucose control among Hispanics with T2D. Prospective studies are needed to confirm causality and long-term effects.

Association of a dietary inflammatory index with cardiometabolic, endocrine, liver, renal and bones biomarkers: cross-sectional analysis of the UK Biobank study

BACKGROUND AND AIMS

Research into the relationship between an Energy-adjusted Diet-Inflammatory Index (E-DII) and a wider health-related biomarkers profile is limited. Much of the existing evidence centers on traditional metabolic biomarkers in populations with chronic diseases, with scarce data on healthy individuals. Thus, this study aims to investigate the association between an E-DII score and 30 biomarkers spanning metabolic health, endocrine, bone health, liver function, cardiovascular, and renal functions, in healthy individuals.

METHODS AND RESULTS

66,978 healthy UK Biobank participants, the overall mean age was 55.3 (7.9) years were included in this cross-sectional study. E-DII scores, based on 18 food parameters, were categorised as anti-inflammatory (E-DII < -1), neutral (-1 to 1), and pro-inflammatory (>1). Regression analyses, adjusted for confounding factors, were conducted to investigate the association of 30 biomarkers with E-DII. Compared to those with an anti-inflammatory diet, individuals with a pro-inflammatory diet had increased levels of 16 biomarkers, including six cardiometabolic, five liver, and four renal markers. The concentration difference ranged from 0.27 SD for creatinine to 0.03 SD for total cholesterol. Conversely, those on a pro-inflammatory diet had decreased concentrations in six biomarkers, including two for endocrine and cardiometabolic. The association range varied from -0.04 for IGF-1 to -0.23 for SHBG.

CONCLUSION

This study highlighted that a pro-inflammatory diet was associated with an adverse profile of biomarkers linked to cardiometabolic health, endocrine, liver function, and renal health.

Comparison of Intake of Food Groups Based on Dietary Inflammatory Index (DII) and Cardiovascular Risk Factors in the Middle-Age Population of Lower Silesia: Results of the PURE Poland Study

Chronic inflammation is involved in the pathogenesis of many non-infectious diseases, including cardiovascular diseases (CVD), a leading cause of death in Europe. The aim of the study was to assess the inflammatory potential of the diets of participants enrolled in the Polish arm of the Prospective Urban and Rural Epidemiological (PURE) study, evaluate the association between the dietary inflammatory index (DII) score with the dietary content, and to determine the correlation of DII score with selected anthropometric parameters and biochemical risk factors for CVD. Diets were assessed with the Food Frequency Questionnaire (FFQ). Among participants with pro-inflammatory diets, we reported higher mean values of triglycerides (TG), fasting glucose (FG), atherogenic index of plasma (AIP), and the Castelli's risk index (CRI) in the group of men and women, and higher waist circumference (WC) and waist-to-hip ratio (WHR) in the group of women. Pro-inflammatory diets were associated with higher intake of refined grains, sweets, juices, red meat, high-fat cheese and cream, alcohol, fats except for vegetable oils, potatoes, sugar and honey, French fries, fried fish, and processed/high-fat poultry. Moreover, study participants with pro-inflammatory diets consumed more milk, low-fat dairy, and eggs associated with unhealthy dietary habits, but this should not be considered as an independent CVD risk factor. Anthropometric and biochemical outcomes were more favorable among study participants who consumed more vegetables, fruits, nuts, seeds, raisins, pulses, low-fat poultry, and tea. However, association of beverage consumption with dietary inflammatory potential requires further study.

Pro-inflammatory diet, cardio-metabolic risk factors and risk of type 2 diabetes: A cross-sectional analysis using data from RaNCD cohort study

BACKGROUND

Inflammation and cardiometabolic risk factors can be involved in developing type 2 diabetes mellitus (T2DM). This study aimed to investigate and compare the association between a pro-inflammatory diet and cardiometabolic risk factors in patients with T2DM and non-T2DM cases.

METHODS

In this cross-sectional population-based study, considering the baseline data of the Ravansar Non-Communicable Disease (RaNCD) cohort, patients with T2DM (n = 785) and non-T2DM cases (n = 8254) were included. The dietary inflammatory index (DII) was calculated using a food frequency questionnaire (FFQ) and was classified into four groups (quartiles) with lowest to highest scores. Logistic regression analysis was conducted to determine the association between DII and cardiometabolic risk factors in both groups.

RESULTS

The participants were 9,039 (4140 men and 4889 women) with a mean age of 47.4 ± 8.2 years; the mean body mass index (BMI) and DII were 27.49 ± 4.63 kg/m² and - 2.49 ± 1.59, respectively. After adjustment for confounding factors, we found that DII can increase the risk of T2DM by 61% (95% CI 1.27 to 2.05, P < 0.001). A comparison of two groups revealed that the association of DII, obesity/overweight and dyslipidemia were also significant in both diabetic (P < 0.05) and non-diabetic cases (P < 0.05). However, no significant association was found between DII, MetS, and hypertension in either of the groups. The association between DII and cardiovascular diseases (CVDs) was only significant in diabetic patients (1.65; 95%CI: 1.02 to 2.65, P = 0.04) and T2DM showed an interaction with the association between DII and CVDs.

CONCLUSION

Inflammatory potential of diet may increase the risk of T2DM. Although it can increase the risk of some cardiometabolic risk factors in both diabetic and non-diabetic cases, its effects were greater among patients with T2DM. However, further prospective studies are required to confirm these associations.

The Dietary Inflammatory Index is positively associated with cardiometabolic risk parameters in atherosclerosis patients

Inflammation is implicated in the development and progression of a variety of cardiovascular diseases. We hypothesized that the Dietary Inflammatory Index (DII) is associated with anthropometric indices and metabolic parameters in Iranian atherosclerosis patients. The present cross-sectional study was conducted on 320 Iranian atherosclerosis patients. The DII was estimated using a valid and reliable 168-item food frequency questionnaire. Odds ratios and 95% confidence intervals were evaluated for anthropometric indices and metabolic parameters according to the DII score. Linear regression was used to estimate the relationship between DII scores with atherosclerosis-related dependent variables. According to the continuous score of DII, there was no significant association between DII and odds of obesity, total cholesterol/high-density lipoprotein cholesterol ratio, and aspartate aminotransferase/alanine aminotransferase ratio in all 3 models (P ≥ .05). In linear regression analysis, we found a significant association between DII score and fasting blood sugar, lipid profile (except for high-density lipoprotein cholesterol), liver enzymes (except for alkaline phosphatase), and serum sodium in adjusted models (P < .05). In this study, patients with atherosclerosis consuming a pro-inflammatory diet was positively associated with fasting blood sugar, lipid, and liver enzymes measures. Future studies with prospective and interventional designs are required to clarify the association between this dietary index and cardiovascular disease risk factors among patients with atherosclerosis.

A new mathematical approach to improve the original dietary inflammatory index (DII) calculation

Accumulating evidence links dietary intake to inflammatory processes involved in non-communicable disease (NCD) development. The dietary inflammatory index (DII) designed by Shivappa et al. has been shown to capture the inflammatory potential of dietary behavior in a large number of epidemiological studies. Thus, the DII may serve as future tool to assess someone's nutritional inflammatory capacities and hence, the individual risks for NCD development later in life. The calculation method of the DII, however, can benefit from alternative mathematical steps, particularly regarding the transformation from standardized daily food consumption to percentile scores. Here, we provide novel approaches, the scaling-formula (SF) and scaling-formula with outlier detection (SFOD) methods, with the aim to optimize the DII calculation method proposed by Shivappa and colleagues. We illustrate on simulated data specific limitations of the original DII calculation and show the benefits of the SF/SFOD by using simulated data and data from the prospective TEENDIAB study cohort, which supports the application of SF/SFOD in future epidemiological and clinical studies.