Early glycemic control is crucial for improving outcomes and reducing mortality in individuals newly diagnosed with type 2 diabetes (T2D). Lifestyle modifications, primarily dietary changes, play a significant role in achieving this control. Dietary intake affects glycemic control by influencing weight and glucose-insulin homeostasis. Several dietary patterns, including the Mediterranean diet, DASH diet, and low-glycemic index diets, have demonstrated effectiveness in improving glycemic control and other health outcomes in people with T2D. However, existing research largely relies on cross-sectional studies. This study aims to quantify short-term improvements in diet quality and identify associated factors after T2D diagnosis using data from the 3D study, a longitudinal case series study following a national sample of Australians newly diagnosed with T2D over 12 months. The DASH diet was chosen as the dietary pattern for assessing diet quality due to its demonstrated benefits for glycemic control, blood pressure, and other relevant health markers in individuals with T2D. The study will build upon previous cross-sectional research that revealed people with T2D frequently have poor-quality diets, highlighting the need for strategies to support healthy eating in this population.

Previous research has highlighted the importance of diet quality in managing type 2 diabetes and its impact on glycemic control, weight management, and other health outcomes. Studies have shown the effectiveness of various dietary patterns, including the DASH diet, in improving these aspects. However, the literature predominantly consists of cross-sectional studies, limiting understanding of dietary changes immediately following diagnosis. A systematic review conducted by the authors themselves found that people with T2D often don't meet recommended intakes for fruits, vegetables, whole grains, and low-fat dairy, while exceeding recommendations for red meat. This underscores the need for prospective studies to examine immediate dietary changes after diagnosis and to identify the demographic and health factors related to these changes. This current study aims to fill this gap in the literature.

The 3D study employed a case-series design, following 225 Australian adults newly diagnosed with T2D (within 6 months prior to recruitment) via five interviewer-administered telephone surveys at baseline, 3, 6, 9, and 12 months. This paper focuses on baseline and 3-month data. Dietary data was collected using the Automated Self-Administered 24-h Dietary Assessment Tool (ASA-24) and analyzed using FoodWorks software to calculate DASH scores (range 8-40). Other data included demographic information, smoking status, BMI, waist circumference, physical activity levels (IPAQ short form), medication use, and mental health (K10 questionnaire). Participants were categorized into 'improvers' (DASH score increase of ≥3 points) and 'maintainers' (≤2 points improvement, no change, or decrease). Statistical analyses included Pearson's χ², Fisher's exact tests, independent sample t-tests, ANOVA, Mann-Whitney U, and Kruskal-Wallis tests to compare baseline characteristics between groups and assess changes in DASH scores. The representativeness of the 3D sample was compared with national data from several other Australian studies, using Pearson's χ² goodness of fit tests. Data cleaning involved double-checking 10% of records and outlier verification. The study was registered with the Australian New Zealand Clinical Trials Registry (ANZCTR) and approved by the Griffith University Human Research Ethics Committee.

The study sample (n=225) was comparable to other Australian diabetes cohorts in terms of gender and BMI, but differed in age, remoteness, and socioeconomic status. The mean baseline DASH score was 24.4 (SD 4.7). Thirty-one percent of participants improved their DASH score by at least 3 points within 3 months. 'Improvers' had significantly lower baseline DASH scores (p<0.001), lower BMI (p=0.045), higher physical activity levels (p=0.028), and were less likely to smoke (p=0.018) compared to 'maintainers'. No significant differences in demographic characteristics were found between the groups. Gender-stratified analysis revealed that the significant effect of BMI on diet quality improvement was only observed in male participants (p=0.048). The lower mean baseline DASH score in the 'improvers' group resulted from lower intake of fruits, vegetables, low-fat dairy, and whole grains, and higher intake of sugar-sweetened beverages, red and processed meats, and nuts and legumes compared with 'maintainers'.

This study provides novel longitudinal data on diet quality changes immediately following a T2D diagnosis. The finding that lifestyle factors (physical activity, smoking, BMI) are associated with improvements in diet quality suggests that interventions focusing on these areas could effectively support individuals in achieving better dietary habits after diagnosis. The lack of association between demographic characteristics and diet quality changes highlights the importance of addressing lifestyle behaviors rather than solely focusing on demographic factors in interventions. The relatively low proportion of participants achieving clinically significant improvement in diet quality (31%) may be due to several factors including pre-diagnosis changes, the time lag between diagnosis and baseline assessment, or the limitations of the 24-h dietary recall method. The finding that men with higher BMIs had more difficulty improving their diet quality underscores the need for tailored interventions that address the unique challenges faced by different subgroups. The importance of combined interventions targeting diet and physical activity, particularly for smokers, is highlighted.

This study demonstrates that short-term improvements in diet quality after T2D diagnosis are strongly linked to lifestyle behaviors, particularly physical activity, smoking status, and BMI (in males). Demographic factors do not appear to significantly influence these early dietary changes. Future research should investigate the long-term impact of these initial dietary changes on health outcomes and develop tailored interventions that incorporate these lifestyle factors to maximize the effectiveness of dietary management strategies in people with T2D.

The study's limitations include the relatively low response rate (2.9%), which might introduce non-response bias. The use of a single 24-h dietary recall to assess dietary intake may not fully capture the variability of dietary habits. Underrepresentation of Aboriginal and Torres Strait Islander people and non-English speakers could limit generalizability. The lag time between diagnosis and baseline data collection might have influenced the results. Finally, the cross-sectional nature of the comparisons with national data means that any observed differences can only suggest areas for further exploration; it does not confirm actual representativeness of the 3D cohort.