Ultra-processed foods and the incidence of pre-diabetes and type 2 diabetes among Iranian adults: the Tehran lipid and glucose study.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-10-09 DOI:10.1186/s12986-024-00854-4

Nazanin Moslehi, Maryam Mahdavi, Parvin Mirmiran, Fereidoun Azizi

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Abstract

Background: No study has investigated the association between ultra-processed food (UPF) and pre-diabetes development. Furthermore, prior investigations on the association between UPF and the risk of type 2 diabetes (T2D) were primarily conducted in Europe and America, and studies in other regions are lacking. We investigated the association between ultra-processed foods and the risk of pre-diabetes and T2D in a cohort of Iranians.

Methods: This prospective study, with a sample size of 1954 for pre-diabetes and 2457 for T2D, was conducted among adults' participants (aged ≥ 18 years) from the Tehran Lipid and Glucose Study (TLGS). We defined UPF intake using NOVA calcification as a proportion of total energy, and calculated its average intake during the follow-ups. The hazard ratios (HR) and 95% confidence intervals (95% CI) for pre-diabetes/T2D across tertiles of total UPF and per 10% of its increment were examined using Cox proportional hazards models. We also investigated the possibility of non-linear association using a restricted cubic spline regression.

Results: We identified 766 and 256 cases of pre-diabetes and T2D, respectively, during a median follow-up of 7 years for pre-diabetes and 8.6 years for T2D. In the multivariable adjusted model, a 10% increase in total UPF intake was associated with a 12% higher risk of pre-diabetes (HR = 1.12; 95% 1.02, 1.23). The incidence of pre-diabetes was also higher in those in tertile 3 than those in tertile 1 (HR = 1.28; 95% CI = 1.07, 1.52). Following additional adjustment for diet quality, the results remained unchanged. Spline regression demonstrated a J-shaped association between UPF and the risk of pre-diabetes; the risk of pre-diabetes did not increase until UPF consumption exceeded about 24% of total energy intake. Of the individual UPF, hydrogenated fat/mayonnaise/ margarine group was related to an increased risk of pre-diabetes. The total UPF and its individual items were not associated with T2D.

Conclusions: This study found a positive, non-linear relationship between total UPF and the risk of pre-diabetes in Iranian adults. Our data could not show any significant association between UPF and T2D risk.

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超加工食品与伊朗成年人中糖尿病前期和 2 型糖尿病的发病率：德黑兰血脂和血糖研究。

背景：还没有研究调查过超高加工食品（UPF）与糖尿病前期发展之间的关系。此外，之前关于超高加工食品与 2 型糖尿病（T2D）发病风险之间关系的调查主要在欧洲和美洲进行，而在其他地区则缺乏研究。我们在伊朗人队列中调查了超标加工食品与糖尿病前期和 T2D 风险之间的关系：这项前瞻性研究是在德黑兰血脂和血糖研究（TLGS）的成人参与者（年龄≥ 18 岁）中进行的，糖尿病前期和 T2D 患者的样本量分别为 1954 人和 2457 人。我们用 NOVA 钙化率占总能量的比例来定义 UPF 摄入量，并计算了随访期间的平均摄入量。我们使用 Cox 比例危险模型检验了总 UPF 各层次以及每增加 10%，糖尿病前期/T2D 的危险比 (HR) 和 95% 置信区间 (95%CI)。我们还使用限制性立方样条回归法研究了非线性关联的可能性：我们分别发现了 766 例和 256 例糖尿病前期和 T2D 患者，糖尿病前期患者的中位随访时间为 7 年，T2D 患者的中位随访时间为 8.6 年。在多变量调整模型中，UPF总摄入量每增加10%，糖尿病前期的风险就会增加12%（HR = 1.12; 95% 1.02, 1.23）。三等分中的糖尿病前期发病率也高于三等分中的糖尿病前期发病率（HR = 1.28; 95% CI = 1.07, 1.52）。在对饮食质量进行额外调整后，结果保持不变。样条回归显示，UPF与糖尿病前期风险之间存在J形关系；UPF摄入量超过总能量摄入的24%时，糖尿病前期风险才会增加。在单项 UPF 中，氢化脂肪/蛋黄酱/人造奶油组与糖尿病前期风险增加有关。总的 UPF 及其单项与 T2D 无关：本研究发现，伊朗成年人的总 UPF 值与糖尿病前期风险之间存在非线性的正相关关系。我们的数据并未显示出 UPF 与 T2D 风险之间存在任何重大关联。

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来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

审稿时长

4-8 weeks

期刊介绍： Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.