*3.4. Body Composition*

The DASH diet was consistently associated with several adiposity measures (Table 5). At the follow-up visit, higher DASH scores were associated with lower BMI (β = −1.64, 95% CI: −3.17, −0.11), body fat percent (β = −1.79, 95% CI: −3.01, −0.57), and fat mass to height ratio (β = −1.09, 95% CI: −3.27, −0.61) at the same visit, and increases in DASH between visits were also inversely associated with change in BMI (β = −1.64, 95% CI: −2.92, −0.36) and body fat percent (β = −1.62, 95% CI: −2.02, −0.17). Similar inverse associations were observed between DASH and measures of central adiposity, including trunk to limb ratio and VAT.

The DII was positively associated with body fat percent in the cross-sectional baseline analyses (Table 5). Though not statistically significant, the DII was also positively associated with most adiposity measurements at both visits, and positive change in DII was associated with positive changes in adiposity from baseline to follow-up.


**Table 5.** Estimated effect size and 95% CI for the effect of 1 standard deviation increase in diet score on body composition.

<sup>1</sup> Model A: outcome ~ diet score + covariates. <sup>2</sup> Model B: Δoutcome ~ Δdiet score + covariates. Model A covariates: HEI, DASH, and DII models adjusted for age, sex, ethnicity, physical activity, and parental education. MDS models adjusted for energy intake, age, ethnicity, physical activity, and parental education. Model B covariates: Model A covariates + baseline diet score. Effects were scaled to 1 standard deviation of exposure. Abbreviations: BMI: body mass index; FFMI: fat-free mass index; VAT: visceral adipose tissue.

#### *3.5. Sensitivity Analyses*

Results from the sensitivity analyses are reported in Supplemental Tables S1–S3. Models that did not adjust for physical activity had slightly larger effect estimates for the relationship between HEI and DASH and impaired glucose tolerance compared to models that did adjust for physical activity. There was little effect on risk for prediabetes/T2D, and the main findings were the same in the physical activity-adjusted and -unadjusted models. Adjustment for body fat percent also had little effect on the relationships between HEI or DASH and prediabetes/T2D, suggesting that it may not mediate the relationship between diet and prediabetes/T2D. However, in most cases, controlling for body fat percent attenuated the effects of each diet on all other glucose outcomes.

BMI, body fat percent, FFMI, fat mass to height ratio, and VAT were significantly associated with increased risk for prediabetes/T2D at all time points (Table S4). At the follow-up visit only, android to gynoid ratio, trunk to leg ratio, and trunk to limb ratio were also positively associated with prediabetes/T2D.

#### **4. Discussion**

We observed strong inverse associations both in cross-sectional and longitudinal analyses between the HEI and DASH diet and risk of prediabetes/T2D. We also found negative associations between the HEI and DASH diet and 2-h glucose, HbA1c, fasting glucose, and glucose AUC at both visits and in the longitudinal analysis though these relationships were not all statistically significant. The MDS was not consistently associated with prediabetes/T2D, glucose measurements, or body composition. We also observed inverse relationships between HEI, DASH, and MDS with measures of adiposity and body composition, suggesting that high diet quality may be protective against obesity and adverse accumulation of adipose tissue. The period between late adolescence to early adulthood is one of transition, where young people begin to live independently and gain more control of their lifestyles. However, there are limited assessments of change in diet quality during this transition [43], and these results emphasize the importance of considering diet quality in T2D risk within this age group.

To our knowledge, no other study has evaluated the longitudinal relationship between glucose dysregulation and HEI, DASH, MDS, and DII in young adults. Several metaanalyses have summarized the relationship between diet quality and type 2 diabetes, prediabetes, or other measures of glucose dysregulation in older adults. These analyses consistently report strong protective effects of healthy dietary patterns, including the DASH and HEI [10,13,15]. However, previous reviews found effects of similar magnitude between the HEI, DASH, and MDS [14], whereas we report a larger protective effect associated with increases in DASH diet adherence across both visits compared to either the HEI or MDS. The DII has been inconsistently associated with risk of T2D in older adults [17,18] though inflammation is involved in the pathogenesis of type 2 diabetes [44]. Like Vahid (2017), we observed positive associations between DII and impaired glucose intolerance and prediabetes.

Diet is also a risk factor for obesity, which is itself a significant driver of the T2D epidemic in both adults and youth [6,45,46], and increases in body fat greatly increase the risk for future diabetes [47]. Accumulation of visceral fat is also linked to T2D development and severity [48,49]. Our study found similar effects, with multiple adiposity indices significantly associated with increased risk of prediabetes/T2D. Our findings also suggest an inverse relationship between high diet quality and central obesity, with HEI and DASH consistently associated with android to gynoid fat ratio, trunk to limb fat ratios, and VAT. There also appeared to be positive associations between DII and adiposity and visceral fat measures. These findings suggest that high quality diets may reduce the risk of type 2 diabetes in part by reducing total body and visceral fat.

This study has several strengths. Participants were recruited from the Southern California Children's Health Study [30], which allowed detailed measures of glucose metabolism, diet, body composition, and lifestyle factors. OGTT and DEXA provide highly detailed information about glucose metabolism and body composition, respectively, beyond that of fasting plasma glucose, HbA1c, or BMI alone [50,51]. 2-h glucose and glucose AUC, for example, assess glucose tolerance, and impaired glucose tolerance is an early sign of glucose dysregulation and type 2 diabetes risk not often captured in clinical settings [52]. Additionally, exposures and outcomes were assessed at both visits, which allowed us to examine associations across time. Despite this, we note some limitations. Two systems were used to collect 24-h dietary recalls: the NDSR at baseline and the ASA24 at follow-up. We are not aware of any evidence that this difference would introduce bias away from the null, and any misclassification of diet is expected to be nondifferential and independent of prediabetes/T2D status. It is also common for studies involving multiple cohorts to integrate different diet assessment measures [53,54]. There is a possibility that residual confounding contributed to our reported effects; family history of T2D, maternal obesity, and low birthweight are also associated with young-onset T2D though they are less likely to be associated with diet. However, the magnitude of the relationships we report are large, and any confounding by these or other factors are unlikely to account for the entire effect. Additionally, our sample size for the longitudinal analysis was 85, limiting the statistical power to detect significant relationships. Limitations of one of the DEXA machines used at baseline also limited the available sample size for some adiposity measurements (e.g., android to gynoid fat ratio, trunk to limb fat ratio). However, power was sufficient to identify strong, statistically significant, protective effects of high-quality diets on prediabetes risk.

The COVID-19 pandemic may also have affected our recruitment efforts for the followup visit. Our recruitment began as the SARS-CoV-2 virus (COVID-19) was declared first a Public Health Emergency and then a pandemic [55]. The resulting disruptions to daily life would have affected our participants and likely impacted lifestyle factors such as physical activity, sleep, and eating habits as well as stress, social supports, and physical health, all of which may affect non-communicable disease risk [55–57]. It is possible that the observed decreases in diet quality between the baseline and follow-up visits may be, in part, due to the pandemic. Even if some of the change in diet were due to changes in lifestyle associated with the COVID-19 pandemic, our findings emphasize the importance of maintaining a healthy diet to reduce the risk for T2D.

Our results indicate that improvements in adherence to the HEI and DASH dietary patterns may reduce risk for T2D. Though both measure diet quality, the construction of each index emphasizes different nutrients and food groups, and there are several ways in which an individual may improve their score and overall diet quality. For example, the HEI rewards greater adherence to the USDA Dietary Guidelines for Americans with higher scores on a 100-point scale [36]. To improve a HEI score, one has several options: (1) increase intake of one or several food groups (fruit, vegetables, seafood, etc.) to the levels recommend by the USDA; (2) reduce intake of added sugars and salt as recommended by the USDA; or (3) reduce the proportion of total grains that come from refined sources or increase the proportion of dietary fats that are mono- or polyunsaturated [58]. Similarly, improvements in DASH diet score could be achieved by reducing consumption of saturated fat, cholesterol, or sodium, or by increasing fiber, magnesium, potassium, and calcium intake [37]. By encouraging changes to overall dietary patterns rather than emphasizing specific foods or nutrients (i.e., kilocalories, sugar-sweetened beverages), individuals may have more flexibility in their choice of dietary habits to alter or methods of alteration, leading to more successful behavior change [59,60].
