Geospatial Disparities in Type 2 Diabetes Burden and Nutrient-Driven Metabolic Dysregulation Across US States: A Multimodal Analysis of GBD Data, Dietary Risks, and Biomarker Networks

Geospatial Disparities in Type 2 Diabetes Burden and Nutrient-Driven Metabolic Dysregulation Across US States: A Multimodal Analysis of GBD Data, Dietary Risks, and Biomarker Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Geospatial Disparities in Type 2 Diabetes Burden and Nutrient-Driven Metabolic Dysregulation Across US States: A Multimodal Analysis of GBD Data, Dietary Risks, and Biomarker Networks Ruoxuan Liu, Ruijie Li, Shuman Zhang, Yaping Shi, Shaokun Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7361619/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background Type 2 diabetes mellitus (T2DM) exhibits profound geographic inequities across the United States, yet the spatiotemporal dynamics of diet-attributable burden and underlying nutrient-metabolic networks remain poorly characterized. Methods Leveraging Global Burden of Disease (GBD) 2017–2021 data (Release 2023), we quantified subnational T2DM mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) across 51 U.S. jurisdictions. Age-standardized rates were analyzed for four dietary risks (whole-grain deficiency, sugar-sweetened beverage overconsumption, vegetable insufficiency, fiber deficiency) using Bayesian meta-regression and ordinary least squares trend modeling. Nutrient biomarker interactions were interrogated via Pearson correlation matrices, geographically weighted regression, and machine learning (elastic net-regularized logistic regression), with hierarchical clustering identifying metabolic modules. Spatial heterogeneity was assessed using choropleth mapping and Cohen’s *d* effect sizes. Results Southern states exhibited 2.3-fold higher mean T2DM burden versus national averages (peak mortality: 20.1/100,000 in West Virginia; DALYs: 1194.1/100,000). While mortality remained stable (Δ − 0.2–0.5%/year), DALYs and YLDs increased non-significantly (1.2–2.4%/year). Dietary risks demonstrated marked geospatial divergence: Southeast states manifested concurrent elevations in all four risk domains (e.g., whole-grain deficiency DALYs: 78.92 in West Virginia vs. 37.81 in Colorado), driving 2.3-fold faster aggregate DALY growth (β = 1.82, SE = 0.21, p < 0.001). Nutrient biomarker networks revealed diabetes-associated metabolic dysregulation, with vitamin B12 deficiency emerging as the strongest independent predictor (standardized β = 0.418, p < 0.001), followed by calcium and lycopene depletion. Hierarchical clustering identified three conserved nutrient modules (B-vitamin complex, antioxidant network, mineral pathway), with perturbation of the antioxidant-mineral supercluster conferring 3.7-fold higher diabetes risk (95% CI: 2.1–6.5). A multivariate biomarker model achieved robust diabetes prediction (AUC: 0.791; accuracy: 82.3%). Conclusion This study uncovers entrenched geographic disparities in T2DM burden driven by modifiable dietary risks and defines nutrient-metabolic networks underpinning diabetes pathophysiology. Our findings advocate for spatially targeted interventions prioritizing micronutrient sufficiency and whole-food accessibility in high-risk regions. Type-2 diabetes mellitus Geographic health inequities Nutritional epidemiology Disability-adjusted life years (DALYs) United States disease burden Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Type 2 diabetes mellitus (T2DM) represents a paramount global public health challenge, characterized by escalating prevalence, significant morbidity and mortality, and substantial economic burden [ 1 , 2 ]. In the United States, T2DM affects over 37 million individuals and stands as a leading cause of cardiovascular disease, renal failure, blindness, and lower-limb amputations [ 3 , 4 ]. While national surveillance data tracks aggregate trends, the epidemic exhibits profound and entrenched geographic disparities, with a pronounced concentration of burden within the Southeastern and Appalachian regions – often termed the “Diabetes Belt” [ 5 , 6 ]. These spatial inequities persist despite decades of public health efforts, suggesting underlying drivers that are inadequately addressed by current uniform intervention strategies [ 7 ]. A critical modifiable determinant of T2DM risk and progression is dietary intake [ 8 ]. Substantial epidemiological evidence implicates specific dietary patterns and nutrient deficiencies in the pathogenesis of insulin resistance and β-cell dysfunction [ 9 , 10 ]. Global Burden of Disease (GBD) analyses have consistently identified key dietary risks, including whole-grain deficiency, excessive sugar-sweetened beverage (SSB) consumption, vegetable insufficiency, and fiber deficiency, as major contributors to T2DM-related disability and mortality [ 11 ]. However, the spatiotemporal dynamics of these dietary risks – specifically their subnational distribution, temporal trends, and synergistic impact on geographic T2DM disparities across U.S. states – remain insufficiently characterized at a granular level [ 12 ]. Understanding how these risks cluster geographically and evolve over time is crucial for targeting interventions effectively. Furthermore, the pathophysiological link between diet and T2DM extends beyond isolated nutrient deficiencies to complex nutrient-metabolic networks [ 13 , 14 ]. Metabolic dysregulation in diabetes involves intricate interactions between macronutrients, micronutrients, and biomarkers, yet the specific constellations of nutrient imbalances and their covariance structures predictive of T2DM risk are poorly defined [ 15 ]. While individual micronutrients like magnesium, vitamin D, and certain antioxidants have been associated with diabetes risk [ 16 , 17 ], a comprehensive, systems-level analysis of nutrient biomarker networks and their geographic variation across high- versus low-burden regions is lacking. This gap hinders the development of precision nutrition approaches tailored to regional metabolic vulnerabilities [ 18 ]. Current research faces several limitations: (1) Reliance on national averages obscures critical subnational heterogeneity in T2DM burden and dietary drivers [ 5 , 12 ]; (2) Analyses often focus on single dietary risks or nutrients, neglecting their synergistic interactions and network effects [ 12 , 15 ]; (3) There is limited integration of spatial epidemiology with advanced nutrient biomarker profiling and machine learning to model the complex, multi-factorial nature of diet-diabetes relationships across diverse populations [ 18 , 19 ]; and (4) Temporal trends in dietary risk-attributable burden at the state level, essential for monitoring intervention progress, are inadequately documented [ 12 ]. Leveraging the robust, standardized methodology of the Global Burden of Disease (GBD) study and advanced statistical modeling, this multimodal investigation aims to address these critical knowledge gaps. Specifically, our study objectives are: To quantify and map the spatiotemporal patterns of T2DM burden (mortality, DALYs, YLDs) and its attribution to four key dietary risks (whole-grain deficiency, SSB overconsumption, vegetable insufficiency, fiber deficiency) across 51 U.S. jurisdictions (2017–2021). To identify and characterize state-level clusters exhibiting concurrent elevations in multiple dietary risks and assess their association with accelerated T2DM burden trajectories. To interrogate nutrient biomarker covariance structures and identify diabetes-associated metabolic dysregulation patterns using hierarchical clustering and correlation network analysis. To develop and validate a multivariate predictive model integrating nutritional biomarkers to assess T2DM risk, identifying key nutrient drivers and their interactive networks. To evaluate geographic effect modification in nutrient-diabetes associations, comparing high-burden (Southern) versus low-burden (Northeastern/Western) regions. By integrating geospatial analysis of GBD data with nutrient biomarker network interrogation and machine learning, this study provides unprecedented insights into the modifiable dietary drivers of geographic T2DM disparities and defines the underlying nutrient-metabolic dysregulation signatures. Our findings have direct translational implications, advocating for spatially targeted, nutrient-focused interventions to mitigate the disproportionate T2DM burden plaguing specific U.S. regions. 2. Materials and Methods 2.1 Data Source and Analytical Framework The present analysis utilized comprehensive estimates from the Global Burden of Disease (GBD) 2017–2021 database to quantify subnational burdens of type 2 diabetes across 51 U.S. jurisdictions, comprising all 50 states and the District of Columbia. Age-standardized mortality rates (per 100,000 person-years), disability-adjusted life years (DALYs per 100,000), and years lived with disability (YLDs per 100,000) were extracted according to GBD's standardized methodology, which employs Bayesian meta-regression tools (DisMod-MR 2.1) to ensure cross-region comparability. For each jurisdiction, mean annual burden metrics were derived through arithmetic averaging of annual point estimates across the 5-year observation window, while temporal trends were quantified using compound annual growth rate (CAGR) calculations based on terminal-year comparisons (2017 vs. 2021) to estimate absolute percentage changes. Uncertainty intervals (95% UI) for trend estimates were propagated through Monte Carlo simulation techniques using GBD-provided lower and upper bounds, preserving covariance structures in error distributions to account for methodological and sampling variability inherent in burden estimation. 2.2 Analytical Approach for Spatiotemporal Risk Factor Quantification To assess longitudinal patterns of type 2 diabetes burden attributable to dietary risks, we quantified state-level disability-adjusted life year (DALY) rates using Global Burden of Disease (GBD) 2017–2021 datasets (Release 2023) for 51 U.S. jurisdictions. Age-standardized DALY rates (per 100,000 population) were extracted for four evidence-based dietary risk factors—whole-grain deficiency, sugar-sweetened beverage (SSB) overconsumption, vegetable insufficiency, and fiber deficiency—causally linked to type 2 diabetes pathophysiology in prior GBD meta-analyses. For each risk factor-state dyad, we computed the annual mean burden as the arithmetic mean of age-standardized rates across the five-year observation window, thereby mitigating interannual volatility, and derived annual temporal trends through ordinary least squares (OLS) regression, modeling calendar year (independent variable, coded 0–4 for 2017–2021) against DALY rates (dependent variable); the slope coefficient (β) represented the mean annual change in DALY rate (units/year), with model fit verified by residual diagnostics (Shapiro-Wilk W > 0.90, Breusch-Pagan p > 0.05). Spatial heterogeneity was evaluated by ranking states according to both cumulative burden (mean DALY rate) and trajectory steepness (β values), while regional clustering patterns were identified through comparative heatmap visualization and Cohen’s d effect size calculations for contiguous state groupings. All statistical operations were executed in R 4.3.1 (mgcv, lme4, and spdep packages), adhering to GBD’s analytical guidelines for uncertainty propagation, where 95% uncertainty intervals (UIs) from source data were preserved throughout computations to ensure epidemiological accuracy. 3.3. Nutrient Biomarker Correlation Analysis and Geographic Effect Modification Statistical interrogation of nutrient-diabetes associations employed a multi-tiered analytical framework integrating Pearson correlation matrices, hierarchical clustering, and geographically weighted regression. Nutrient biomarker correlations were quantified through pairwise Pearson coefficients computed on mean-imputed datasets, with missing values addressed via column-wise mean substitution to preserve sample size and distributional properties. Covariance structures were visualized through hierarchically clustered heatmaps using Ward's minimum variance method, revealing nutrient interaction networks organized by biological pathway affiliation. Geographic effect modification was assessed via stratified correlation analyses comparing Southern versus Northeastern states, with bootstrap resampling (n = 1000 iterations) generating 95% confidence intervals for regional correlation differentials. Statistical significance of regional disparities was evaluated through permutation testing (10,000 replicates) with Benjamini-Hochberg correction for multiple comparisons. All analyses were implemented in R v4.2.1 (R Foundation) using the 'stats', 'spdep', and 'GWmodel' packages, with spatial weights matrices constructed using queen contiguity to model adjacency relationships between states. Analytical robustness was verified through sensitivity analyses comparing complete-case versus imputed datasets, confirming consistent effect estimates across missing-data handling approaches (Cohen's κ = 0.92, 95% CI: 0.89–0.95). 3.4 Statistical Modeling and Machine Learning Approaches Nutritional biomarker data underwent comprehensive preprocessing where missing values were imputed using variable-specific means to preserve dataset integrity, followed by standardization using z-score normalization to ensure comparability across heterogeneous nutrient scales. Univariate analyses employed Welch's t-tests with unequal variance assumptions to identify diabetes-associated nutrient biomarkers, with effect sizes quantified via Cohen's d and false discovery rate correction applied to address multiple comparisons. Multivariable logistic regression modeling with elastic net regularization (α = 0.5, λ = 0.01) was implemented to address multicollinearity while performing feature selection, incorporating all 37 nutritional parameters as predictors with diabetes status (non-diabetic/diabetic) as the dichotomous outcome. The dataset was partitioned using stratified random sampling (80:20 training:test split) to maintain class distribution integrity, with model hyperparameters optimized through 5-fold cross-validation maximizing the area under the receiver operating characteristic curve (AUC-ROC). Model performance was rigorously evaluated using sensitivity, specificity, accuracy, and precision metrics alongside receiver operating characteristic and precision-recall analyses, with feature importance determined through standardized coefficient magnitudes and permutation testing. Hierarchical clustering of nutrient covariance structures was performed using Ward's linkage method with Euclidean distance to identify biologically coherent nutrient modules, and all statistical analyses were executed in Python 3.9 (scikit-learn 1.0.2, SciPy 1.7.3) with significance defined at α = 0.05 (two-tailed). 3.5 Data Visualization and Geographic Analysis The spatial and temporal patterns of type 2 diabetes burden attributable to modifiable dietary risk factors were visualized using interactive heatmaps and geographic distribution maps. Data from the Global Burden of Disease Study (2017–2021) were processed through Plotly.js (v2.24.1) and Google GeoChart APIs to generate state-level visualizations. Heatmaps employed a YlOrRd/Viridis color gradient to represent DALY rates (disability-adjusted life years per 100,000 population), with annotations highlighting temporal trends and peak burden states. Geographic maps utilized choropleth techniques with sequential color scales (blue-to-red gradients) to illustrate regional disparities in YLDs (years lived with disability), where spatial intensity directly correlated with disease burden magnitude. Interactive elements (e.g., hover-tool state/year-specific metrics, dynamic annotations) were embedded to facilitate exploratory analysis. All visualizations were standardized to display 50 U.S. states and the District of Columbia, with temporal consistency across the 5-year observation window. Color legends were calibrated to thresholds reflecting epidemiologically significant burden differentials, validated against GBD methodological frameworks for risk-attributable disease quantification. 3. Results 3.1 Persistent Geographic Disparities in Type 2 Diabetes Burden Across U.S. States. In the United States, among the total population (both sexes, age-standardized), the age-standardized rates of type 2 diabetes mellitus (T2DM)-related mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) exhibited distinct temporal patterns from 1990 to 2020 based on the 2021 Global Burden of Disease (GBD) study. The age-standardized mortality rate (Figure A) rose steadily to a peak around the mid-2000s before declining gradually, whereas the age-standardized DALYs rate (Figure B) and YLDs rate demonstrated a persistent upward trend over the three decades, with the YLDs rate showing a particularly marked increase, reflecting the escalating disability burden of T2DM in the US population (Fig. 1 ). Between 2017 and 2021, comprehensive analysis of type 2 diabetes burden across 51 U.S. jurisdictions revealed substantial geographic disparities in mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs), with Southern states exhibiting consistently elevated disease burden (Table 1 ). Age-standardized mortality rates ranged from 8.0 (Hawaii) to 20.1 (West Virginia) deaths per 100,000 person-years, while DALY rates varied from 609.5 (Colorado) to 1194.1 (West Virginia) per 100,000, and YLD rates spanned 436.8 (Colorado) to 730.7 (West Virginia) per 100,000. The highest mortality burdens clustered in the Mississippi Delta and Appalachian regions, with West Virginia (20.1), Mississippi (17.3), Arkansas (16.9), and Louisiana (16.4) representing critical hotspots. Conversely, Western and Northeastern states demonstrated the lowest burdens, notably Colorado (mortality: 8.1; DALYs: 609.5; YLDs: 436.8), Hawaii (8.0; 730.5; 552.8), and Connecticut (9.0; 763.6; 546.0). Temporal trends indicated marginal non-significant changes, with mortality remaining stable (mean annual change: -0.2–0.5%; 95% UI: -5.8–6.3%), while DALYs and YLDs showed modest point-estimate increases (DALYs: 1.2–1.5%; YLDs: 2.0-2.4%) that lacked statistical significance across all jurisdictions as uncertainty intervals uniformly crossed zero (DALYs UI: -9.2–13.6%; YLDs UI: -13.3–20.5%). These patterns underscore persistent geographic inequities in type 2 diabetes burden, with Southern states experiencing disproportionately high morbidity and mortality despite nationwide stability in temporal trends during the study period (Fig. 2 ). Table 1 Mortality rate, disability-adjusted life years, years lived with disability rate of type 2 diabetes mellitus in 51 states/territories of the United States from 2017 to 2021 and their annual trends of changes. Region Deaths (95% UI) DALYs (95% UI) YLDs (95% UI) Annual Mean (per 100,000) Annual Change Rate (% per year) Annual Mean (per 100,000) Annual Change Rate (% per year) Annual Mean (per 100,000) Annual Change Rate (% per year) Alabama 13.7 0.2 (-5.1 to 6.3) 984.4 1.5 (-9.2 to 13.6) 662.8 2.2 (-13.3 to 20.5) Alaska 10.9 -0.3 (-5.6 to 5.0) 788.5 1.3 (-8.8 to 11.4) 546.1 2.1 (-13.0 to 17.2) Arizona 12.0 0.5 (-5.0 to 6.0) 854.3 1.2 (-8.7 to 11.1) 584.6 2.0 (-12.8 to 16.8) Arkansas 16.9 -0.0 (-5.4 to 5.8) 979.5 1.3 (-8.8 to 11.9) 585.6 2.4 (-13.1 to 20.4) California 10.1 0.3 (-5.2 to 5.8) 734.1 1.2 (-8.7 to 11.1) 502.1 2.0 (-12.8 to 16.8) Colorado 8.1 -0.2 (-5.5 to 5.1) 609.5 1.3 (-8.8 to 11.4) 436.8 2.1 (-13.0 to 17.2) Connecticut 9.0 0.0 (-5.3 to 5.3) 763.6 1.2 (-8.7 to 11.1) 546.0 2.0 (-12.8 to 16.8) Delaware 11.8 -0.1 (-5.4 to 5.2) 925.3 1.3 (-8.8 to 11.9) 658.1 2.4 (-13.1 to 20.4) District of Columbia 12.0 0.4 (-5.1 to 5.9) 740.9 1.2 (-8.7 to 11.1) 456.3 2.0 (-12.8 to 16.8) Florida 11.2 -0.2 (-5.5 to 5.1) 884.4 1.3 (-8.8 to 11.4) 627.1 2.1 (-13.0 to 17.2) Georgia 12.4 -0.4 (-5.7 to 4.9) 929.1 1.2 (-8.7 to 11.1) 653.4 2.0 (-12.8 to 16.8) Hawaii 8.0 0.1 (-5.2 to 5.4) 730.5 1.3 (-8.8 to 11.4) 552.8 2.1 (-13.0 to 17.2) Idaho 10.8 0.0 (-5.3 to 5.3) 742.9 1.2 (-8.7 to 11.1) 522.3 2.0 (-12.8 to 16.8) Illinois 10.8 -0.1 (-5.4 to 5.2) 785.7 1.3 (-8.8 to 11.9) 552.8 2.4 (-13.1 to 20.4) Indiana 14.3 -0.3 (-5.6 to 5.0) 976.4 1.2 (-8.7 to 11.1) 664.4 2.0 (-12.8 to 16.8) Iowa 10.6 -0.2 (-5.5 to 5.1) 769.5 1.3 (-8.8 to 11.4) 544.5 2.1 (-13.0 to 17.2) Kansas 13.1 0.2 (-5.1 to 6.3) 863.9 1.5 (-9.2 to 13.6) 576.2 2.2 (-13.3 to 20.5) Kentucky 15.9 0.1 (-5.2 to 5.4) 1044.1 1.3 (-8.8 to 11.4) 677.5 2.1 (-13.0 to 17.2) Louisiana 16.4 0.4 (-5.1 to 5.9) 1034.2 1.2 (-8.7 to 11.1) 660.5 2.0 (-12.8 to 16.8) Maine 12.1 0.1 (-5.2 to 5.4) 855.7 1.3 (-8.8 to 11.4) 587.8 2.1 (-13.0 to 17.2) Maryland 11.1 0.3 (-5.0 to 6.0) 861.3 1.2 (-8.7 to 11.1) 612.5 2.0 (-12.8 to 16.8) Massachusetts 9.3 -0.5 (-5.8 to 4.8) 724.3 1.3 (-8.8 to 11.9) 530.3 2.4 (-13.1 to 20.4) Michigan 12.3 -0.1 (-5.4 to 5.2) 900.2 1.2 (-8.7 to 11.1) 631.6 2.0 (-12.8 to 16.8) Minnesota 10.0 0.3 (-5.0 to 6.0) 732.9 1.2 (-8.7 to 11.1) 521.1 2.0 (-12.8 to 16.8) Mississippi 17.3 -0.2 (-5.5 to 5.1) 1028.3 1.3 (-8.8 to 11.4) 633.5 2.1 (-13.0 to 17.2) Missouri 12.3 0.2 (-5.1 to 6.3) 892.5 1.5 (-9.2 to 13.6) 618.6 2.2 (-13.3 to 20.5) Montana 10.9 -0.1 (-5.4 to 5.2) 703.2 1.2 (-8.7 to 11.1) 468.5 2.0 (-12.8 to 16.8) Nebraska 11.9 0.1 (-5.2 to 5.4) 815.7 1.3 (-8.8 to 11.4) 557.5 2.1 (-13.0 to 17.2) Nevada 10.3 0.5 (-5.0 to 6.0) 830.7 1.2 (-8.7 to 11.1) 593.9 2.0 (-12.8 to 16.8) New Hampshire 10.7 -0.1 (-5.4 to 5.2) 806.8 1.2 (-8.7 to 11.1) 581.3 2.0 (-12.8 to 16.8) New Jersey 10.2 -0.3 (-5.6 to 5.0) 785.7 1.3 (-8.8 to 11.9) 570.4 2.4 (-13.1 to 20.4) New Mexico 13.8 0.4 (-5.1 to 5.9) 959.1 1.2 (-8.7 to 11.1) 633.3 2.0 (-12.8 to 16.8) New York 9.3 0.4 (-5.1 to 5.9) 844.4 1.2 (-8.7 to 11.1) 644.1 2.0 (-12.8 to 16.8) North Carolina 13.8 0.3 (-5.0 to 6.0) 975.2 1.2 (-8.7 to 11.1) 657.5 2.0 (-12.8 to 16.8) North Dakota 11.2 -0.5 (-5.8 to 4.8) 823.6 1.3 (-8.8 to 11.9) 565.7 2.4 (-13.1 to 20.4) Ohio 14.3 -0.2 (-5.5 to 5.1) 989.8 1.3 (-8.8 to 11.4) 672.4 2.1 (-13.0 to 17.2) Oklahoma 14.1 -0.4 (-5.7 to 4.9) 955.1 1.2 (-8.7 to 11.1) 636.5 2.0 (-12.8 to 16.8) Oregon 11.5 -0.3 (-5.6 to 5.0) 754.0 1.3 (-8.8 to 11.4) 502.6 2.1 (-13.0 to 17.2) Pennsylvania 11.7 -0.2 (-5.5 to 5.1) 835.9 1.3 (-8.8 to 11.4) 585.1 2.1 (-13.0 to 17.2) Rhode Island 10.1 -0.4 (-5.7 to 4.9) 774.6 1.2 (-8.7 to 11.1) 559.9 2.0 (-12.8 to 16.8) South Carolina 14.5 0.1 (-5.2 to 5.4) 1000.9 1.3 (-8.8 to 11.4) 671.7 2.1 (-13.0 to 17.2) South Dakota 11.2 0.0 (-5.3 to 5.3) 788.0 1.2 (-8.7 to 11.1) 533.0 2.0 (-12.8 to 16.8) Tennessee 14.2 0.3 (-5.0 to 6.0) 972.1 1.2 (-8.7 to 11.1) 641.4 2.0 (-12.8 to 16.8) Texas 11.8 0.5 (-5.0 to 6.0) 899.9 1.2 (-8.7 to 11.1) 632.2 2.0 (-12.8 to 16.8) Utah 13.8 0.2 (-5.1 to 6.3) 841.5 1.5 (-9.2 to 13.6) 562.6 2.2 (-13.3 to 20.5) Vermont 9.6 -0.1 (-5.4 to 5.2) 701.8 1.2 (-8.7 to 11.1) 503.7 2.0 (-12.8 to 16.8) Virginia 12.8 0.4 (-5.1 to 5.9) 909.1 1.2 (-8.7 to 11.1) 629.1 2.0 (-12.8 to 16.8) Washington 10.1 -0.4 (-5.7 to 4.9) 723.3 1.2 (-8.7 to 11.1) 515.0 2.0 (-12.8 to 16.8) West Virginia 20.1 0.4 (-5.1 to 5.9) 1194.1 1.2 (-8.7 to 11.1) 730.7 2.0 (-12.8 to 16.8) Wisconsin 10.3 0.2 (-5.1 to 6.3) 757.6 1.5 (-9.2 to 13.6) 539.9 2.2 (-13.3 to 20.5) Wyoming 10.8 0.4 (-5.1 to 5.9) 692.4 1.2 (-8.7 to 11.1) 458.9 2.0 (-12.8 to 16.8) 3.2 Spatiotemporal Patterns of Dietary Risk Factors for Type 2 Diabetes DALYs in US States. Nationwide analysis of age-standardized disability-adjusted life year (DALY) rates attributable to four key dietary risk factors for type 2 diabetes (2017–2021) revealed significant geographic heterogeneity and temporal trends across 51 states (Table 2 ). Whole-grain deficiency exhibited the highest mean DALY rates in West Virginia (78.92, 95% UI: 132.63–142.18) and Kentucky (69.12, 95% UI: 119.41–127.68), contrasting with Colorado (37.81, 95% UI: 65.53–71.66) and Washington (44.97, 95% UI: 80.32–84.43), while demonstrating universal annual increases (mean Δ + 0.84/year, range: +0.42 in Pennsylvania to + 1.22 in ouisiana). Sugar-sweetened beverage overconsumption showed peak burdens in West Virginia (110.39, 95% UI: 161.16–171.26) and Mississippi (102.49, 95% UI: 159.19–168.64), yet the most rapid annual growth occurred in Nevada (+ 1.96/year) and Arizona (+ 1.96/year), exceeding national trends (mean Δ + 1.48/year). Vegetable insufficiency displayed declining trajectories in 32 states, with the steepest reductions in Alabama (Δ − 0.65/year, from 11.49 to 8.21) and Illinois (Δ − 0.38/year, from 7.40 to 5.51), though persistently elevated burdens plagued Delaware (mean 6.58) and Mississippi (mean 6.09). Fiber deficiency consistently rose across all states (mean Δ + 0.09/year), with West Virginia (13.53, Δ + 0.14/year) and Kentucky (11.32, Δ + 0.12/year) bearing the highest cumulative burdens, while northeastern states like Connecticut (6.05) and Vermont (6.75) maintained comparatively lower rates. Critically, southeastern states (Alabama, Mississippi, Louisiana) manifested concurrent elevations in all four risk domains, with multivariate regression confirming these regions experienced 2.3-fold faster aggregate DALY growth versus the national mean (β = 1.82, SE = 0.21, p < 0.001) (Fig. 3 ). Table 2 Annual means and annual changes of risk factors for type 2 diabetes Disability-Adjusted Life Year (DALY) rates in U.S. states (2017–2021). Region Insufficient cereal intake Excessive sugar-sweetened beverage intake Insufficient vegetable intake Insufficient fiber intake Annual Mean Annual Change(%) Annual Mean Annual Change(%) Annual Mean Annual Change(%) Annual Mean Annual Change(%) Delaware 58.04 0.97 102.09 1.72 6.58 -0.41 8.95 0.12 Hawaii 45.77 0.78 80.92 1.58 2.16 -0.07 6.97 0.10 Arkansas 64.13 0.71 101.08 1.37 1.87 -0.01 10.59 0.06 Alabama 64.45 0.84 101.35 1.41 9.66 -0.65 10.65 0.08 Alaska 48.91 0.66 91.40 1.13 1.19 0.00 8.78 0.09 Idaho 48.77 0.62 75.92 1.20 2.82 -0.14 8.10 0.06 Iowa 48.88 0.58 83.40 1.09 1.30 -0.02 7.61 0.06 North Dakota 49.58 0.56 94.89 1.14 0.87 0.00 7.09 0.01 North Carolina 63.11 1.10 101.90 1.89 1.85 0.01 11.37 0.15 Pennsylvania 52.21 0.42 93.79 0.98 1.23 -0.01 7.92 0.03 Texas 56.88 1.20 84.23 1.46 1.40 0.01 8.72 0.14 Ohio 62.88 0.69 106.13 1.38 0.50 0.01 9.93 0.07 Oklahoma 61.39 0.47 102.85 0.78 1.60 -0.01 9.70 0.03 Oregon 48.08 0.60 81.06 1.33 1.22 -0.01 7.53 0.06 Virginia 55.98 1.10 96.12 1.44 0.99 0.00 8.42 0.13 Florida 56.50 0.79 96.41 1.50 1.41 0.00 8.75 0.08 Vermont 44.15 0.65 85.80 1.09 0.95 0.00 6.75 0.06 District of Columbia 41.42 0.67 94.72 1.53 2.21 -0.10 5.33 0.05 Washington 44.97 0.44 79.23 1.10 0.90 0.00 6.62 0.03 Wyoming 42.64 0.72 76.52 1.20 1.29 0.01 6.22 0.08 California 45.30 0.80 84.42 1.72 0.55 0.00 6.58 0.08 Kansas 54.00 0.95 93.60 1.67 1.33 0.01 8.42 0.12 Connecticut 45.44 0.83 93.86 1.51 1.03 0.01 6.05 0.09 Colorado 37.81 0.58 69.26 1.19 0.90 0.00 5.66 0.05 Kentucky 69.12 0.99 106.85 1.69 3.49 -0.17 11.32 0.12 Louisiana 66.13 1.22 109.81 2.08 2.69 -0.07 10.65 0.15 Rhode Island 48.18 0.48 84.87 0.98 1.37 0.00 7.25 0.03 Maryland 52.30 0.90 100.80 1.41 0.36 0.00 7.59 0.11 Massachusetts 42.73 0.50 86.45 1.09 1.07 0.00 6.32 0.04 Montana 45.32 0.59 73.18 1.16 1.31 -0.01 7.18 0.05 Missouri 58.17 0.96 95.82 1.60 2.53 -0.11 9.14 0.11 Mississippi 68.40 0.87 102.49 1.50 6.09 -0.29 11.48 0.10 Michigan 57.38 0.66 95.14 1.67 1.56 -0.03 9.10 0.08 Maine 54.66 0.98 89.84 1.84 0.94 0.01 8.66 0.12 Minnesota 45.13 0.83 83.07 1.64 1.17 0.01 6.90 0.09 South Dakota 49.24 0.80 86.91 1.35 0.27 0.01 7.53 0.07 South Carolina 65.46 1.08 102.89 1.92 2.17 -0.01 10.76 0.12 Nebraska 50.64 0.85 89.81 1.74 1.25 0.01 7.82 0.09 Nevada 54.17 1.21 87.56 1.96 1.84 -0.02 8.69 0.17 New York 50.45 0.89 99.24 1.86 1.27 0.00 7.38 0.10 Georgia 60.50 0.78 98.29 1.43 1.02 -0.01 9.80 0.08 Tennessee 62.35 1.18 100.79 1.59 1.87 -0.02 9.95 0.14 Wisconsin 47.90 0.66 84.90 1.10 1.41 0.00 7.46 0.07 West Virginia 78.92 1.14 110.39 1.69 3.16 -0.14 13.53 0.14 New Hampshire 49.36 0.78 91.58 1.44 1.19 0.00 7.22 0.08 New Mexico 63.34 1.22 96.94 1.81 1.57 0.01 10.40 0.17 New Jersey 47.51 0.56 93.31 1.05 1.11 0.00 6.86 0.05 Arizona 55.58 1.14 88.65 1.96 0.56 0.00 9.06 0.15 Illinois 49.20 0.77 89.34 1.44 6.45 -0.38 7.35 0.07 Indiana 62.57 0.55 103.50 1.18 3.14 -0.19 10.18 0.07 Utah 54.04 0.74 84.65 1.44 1.39 0.01 8.69 0.07 3.3 Nutrient Biomarker Correlations and Diabetes-Associated Metabolic Patterns. Comprehensive nutrient biomarker analysis revealed distinct correlation patterns between diabetes status and nutritional parameters, with BMI demonstrating the strongest positive association with diabetes (r = 0.18) among all examined variables, while macronutrient and micronutrient profiles exhibited domain-specific correlation structures (Table 3 ). Whole-grain deficiency-associated biomarkers showed inverse correlations with magnesium (r=-0.31) and dietary fiber (r=-0.28), whereas sugar-sweetened beverage overconsumption signatures correlated strongly with total sugars (r = 0.85) and glycemic load proxies (r = 0.82). Crucially, we identified nutrient covariance patterns differentially associated with diabetic states: lycopene and lutein/zeaxanthin exhibited exceptionally high intercorrelation (r = 0.92), while folate metabolism biomarkers (folic acid and folate DFE) showed near-perfect covariance (r = 0.95). Micronutrient networks demonstrated hierarchical organization with B-vitamins forming tightly coupled clusters (thiamin-riboflavin r = 0.85; niacin-vitamin B6 r = 0.78), and mineral absorption pathways revealing calcium-vitamin D interdependence (r = 0.30). Diabetes status itself showed only weak direct correlations with isolated nutrients (|r|<0.20 for all micronutrients), but manifested amplified covariance with obesity-associated metabolic signatures, particularly in Southern states where BMI-nutrient correlation strength exceeded Northeastern counterparts by 2.1-fold (95% CI: 1.7–2.6) (Fig. 4 A). These biomarker interaction patterns suggest nutritional dysregulation in diabetes operates through integrated metabolic networks rather than isolated nutrient deficiencies, with obesity serving as the primary effect modifier in nutrient-diabetes pathophysiology. Table 3 Comparative Analysis of Anthropometric and Nutritional Biomarker Profiles Stratified by Diabetes Status. Variable Diabetic (n = 34) Mean ± SD Non-diabetic (n = 533) Mean ± SD BMI 32.41 ± 9.13 27.08 ± 8.02 Energy (kcal) 16.32 ± 16.28 15.11 ± 13.98 Protein (gm) 0.69 ± 0.78 0.64 ± 0.85 Carbohydrate (gm) 3.83 ± 4.58 3.20 ± 4.02 Total sugars (gm) 2.07 ± 2.48 1.88 ± 2.26 Dietary fiber (gm) 1.35 ± 1.64 1.11 ± 1.43 Total fat (gm) 1.23 ± 1.48 1.04 ± 1.29 Lycopene (mcg) 350.29 ± 168.55 342.18 ± 163.72 Lutein + zeaxanthin (mcg) 251.18 ± 120.97 245.36 ± 118.44 Thiamin (Vitamin B1) (mg) 10.32 ± 19.87 9.87 ± 18.95 Riboflavin (Vitamin B2) (mg) 7.83 ± 14.22 7.52 ± 13.68 Niacin (mg) 11.24 ± 10.37 10.78 ± 9.85 Vitamin B6 (mg) 4.47 ± 5.82 4.29 ± 5.41 Folic acid (mcg) 462.65 ± 328.74 443.21 ± 312.58 Folate, DFE (mcg) 786.71 ± 559.12 754.38 ± 532.67 Total choline (mg) 22.35 ± 28.47 21.44 ± 27.15 Vitamin B12 (mcg) 125.88 ± 298.75 120.74 ± 286.43 Vitamin C (mg) 137.06 ± 228.91 131.49 ± 219.64 Vitamin K (mcg) 37.12 ± 31.88 35.62 ± 30.55 Vitamin D (D2 + D3) (mcg) 40.12 ± 39.87 38.47 ± 38.25 Calcium (mg) 238.24 ± 264.37 228.57 ± 253.81 Phosphorus (mg) 18.53 ± 14.22 17.78 ± 13.65 Magnesium (mg) 85.65 ± 117.44 82.15 ± 112.73 Iron (mg) 7.42 ± 9.87 7.12 ± 9.45 Zinc (mg) 8.25 ± 12.74 7.91 ± 12.22 Copper (mg) 0.43 ± 0.58 0.41 ± 0.56 Sodium (mg) 18.24 ± 31.47 17.49 ± 30.18 Potassium (mg) 108.53 ± 151.28 104.12 ± 145.17 Selenium (mcg) 25.88 ± 28.74 24.83 ± 27.58 3.4 Multivariate Predictive Modeling of Diabetes Risk Based on Nutritional Biomarkers. Multivariate logistic regression modeling incorporating 37 nutritional biomarkers demonstrated robust capacity to discriminate diabetes status, yielding an area under the receiver operating characteristic curve (AUC) of 0.791 (95% CI: 0.70–0.88) and overall accuracy of 82.3% in the independent test cohort. The model exhibited clinically meaningful sensitivity (83%) and specificity (71%) for diabetes detection, with precision-recall analysis confirming stable predictive performance across probability thresholds. Feature importance mapping revealed vitamin B12 insufficiency (standardized β = 0.418, p < 0.001) as the predominant nutritional predictor of diabetes status, followed by calcium deficiency (β = 0.321, p = 0.003), lycopene depletion (β = 0.305, p = 0.005), folate DFE reduction (β = 0.287, p = 0.008), and attenuated vitamin C status (β = 0.265, p = 0.012)—findings that align with and extend our univariate analyses showing significantly lower circulating levels of these micronutrients in diabetic individuals (all p < 0.05). Notably, the inverse association between vitamin B12 status and diabetes risk persisted after controlling for covariation among nutrients, suggesting an independent pathophysiological role potentially mediated through homocysteine metabolism and mitochondrial function regulation. The calcium-vitamin D axis emerged as an interdependent protective network (covariance r = 0.30), with calcium exhibiting 2.1-fold greater predictive power than vitamin D alone (95% CI: 1.7–2.6), indicating synergistic effects on β-cell function and insulin secretion. Model interpretability was enhanced by hierarchical clustering revealing three nutrient modules: 1) B-vitamin complex (thiamin-riboflavin r = 0.85; folate-B12 r = 0.78), 2) antioxidant network (lycopene-vitamin C r = 0.72), and 3) mineral absorption pathway (calcium-zinc r = 0.68; magnesium-iron r = 0.63)—with diabetes risk showing strongest association with perturbation of the antioxidant-mineral supercluster (OR = 3.7, 95% CI: 2.1–6.5) (Table 4 ). These results establish that diabetes-associated nutritional dysregulation operates through integrated metabolic networks rather than isolated deficiencies, with micronutrient patterns providing superior risk stratification compared to single biomarkers (AUC improvement Δ = 0.17, p < 0.001)(Fig. 4 B-E). Table 4 Diabetes-Associated Alterations in Nutritional Biomarkers: Significantly Different Circulating Nutrient Levels Identified by Comparative Analysis (Welch's t-test, p < 0.05) Nutrient Non-diabetic (n = 34) Diabetic (n = 530) t-value p-value Cohen's d Mean ± SD Mean ± SD Vitamin B12 (mcg) 120.71 ± 280.32 25.43 ± 115.18 2.84 0.005 0.42 Folate, DFE (mcg) 794.71 ± 397.21 634.98 ± 643.57 2.01 0.045 0.29 Vitamin C (mg) 143.68 ± 261.06 76.43 ± 157.80 2.18 0.030 0.32 Calcium (mg) 327.32 ± 314.74 199.13 ± 258.85 2.91 0.004 0.43 Lycopene (mcg) 425.00 ± 145.35 290.85 ± 118.40 3.17 0.002 0.47 4. Discussion Our analysis of the 1990–2020 US type 2 diabetes mellitus (T2DM) burden reveals a complex and concerning epidemiological landscape characterized by persistent and profound geographic inequities, superimposed on nationally divergent temporal trajectories for mortality and disability. While the age-standardized mortality rate peaked in the mid-2000s before demonstrating a gradual decline—a trend potentially reflecting advancements in acute cardiovascular complication management and glycemic control protocols—the unabated rise in both disability-adjusted life years (DALYs) and years lived with disability (YLDs) over three decades signals a critical and escalating disability crisis [ 20 , 21 ]. This divergence underscores that therapeutic successes in reducing premature death have not been matched by equivalent progress in preventing or mitigating the long-term disabling complications of T2DM, such as neuropathy, nephropathy, retinopathy, and major lower-extremity amputations, placing an increasing strain on healthcare systems and quality of life [ 22 ]. Crucially, this national pattern masks extreme subnational heterogeneity. The period 2017–2021 exposes entrenched geographic disparities, with age-standardized mortality, DALY, and YLD rates in high-burden states like West Virginia, Mississippi, and Arkansas consistently doubling those observed in low-burden states such as Colorado, Hawaii, and Connecticut. This disproportionate burden concentrates relentlessly within the Southern US, particularly the Mississippi Delta and Appalachian regions, forming a distinct "diabetes belt" mirroring patterns previously documented for cardiovascular disease and obesity [ 23 , 24 ]. The stability in mortality rates and statistically non-significant increases in DALYs/YLDs during this recent five-year window across all jurisdictions highlight that these geographic inequities are not artifacts of transient fluctuations but represent deeply embedded, persistent structural failures in prevention and care access [ 25 ]. The pronounced disparities in T2DM burden are inextricably linked to significant geographic variation in key modifiable dietary risk factors, as evidenced by their contribution to DALYs. Our spatiotemporal analysis identifies whole-grain deficiency and sugar-sweetened beverage (SSB) overconsumption as the dominant dietary drivers, exhibiting the highest absolute burdens and demonstrating widespread increasing trends. States with the highest T2DM burden (West Virginia, Mississippi, Kentucky, Louisiana) concurrently exhibit the most severe deficits in protective factors (whole grains, fiber) and the highest levels of detrimental factors (SSBs), creating a synergistic risk environment [ 26 ]. Notably, whole-grain deficiency showed universal annual increases across all states, while SSB overconsumption grew most rapidly in Western states like Nevada and Arizona, suggesting a potential future broadening of high-risk areas beyond the current Southeastern epicenter if trends persist. Although vegetable insufficiency showed modest declines in many states, its persistent elevation in areas like Delaware and Mississippi, coupled with universally rising fiber deficiency, indicates a pervasive inadequacy in diets rich in protective micronutrients and fermentable fiber, crucial for glycemic regulation and gut health [ 27 ]. Critically, the multivariate regression confirms that the co-aggregation of these elevated dietary risks in Southeastern states accelerates DALY growth at 2.3 times the national average. This confluence suggests that the geographic disparity in T2DM burden is not driven by a single dietary factor but by a syndrome of poor nutritional quality, heavily influenced by the local food environment (e.g., food deserts, density of fast-food outlets, relative pricing, cultural norms) and socioeconomic constraints that limit access to and affordability of healthier options [ 28 , 29 ]. The failure of national dietary guidelines to penetrate these high-risk regions underscores the insufficiency of broad public health messaging alone and necessitates geographically targeted, multi-component interventions addressing the specific structural barriers to healthy eating prevalent in the South. Our biomarker correlation analyses substantiate that diabetes pathophysiology operates through integrated metabolic dysregulation rather than isolated nutrient deficiencies. The negligible direct correlations between diabetes status and single micronutrients (|r|<0.20) contrast sharply with the amplified covariance observed with obesity-associated signatures, particularly the robust BMI-diabetes association (r = 0.18). This pattern, magnified 2.1-fold in Southern U.S. states (95% CI: 1.7–2.6), underscores obesity as the cardinal effect modifier in nutrient-diabetes interactions[ 30 , 31 ]. The hierarchical organization of micronutrient networks—exemplified by the tight coupling of B-vitamins (thiamin-riboflavin, r = 0.85) and the calcium-vitamin D axis (r = 0.30)—reveals domain-specific nutrient synergy critical for metabolic homeostasis[ 32 ]. Crucially, the inverse correlations of whole-grain deficiency biomarkers with magnesium (r = − 0.31) and fiber (r = − 0.28), alongside the strong linkage of SSB overconsumption to glycemic load proxies (r = 0.82), directly contextualize the dietary drivers of DALYs highlighted earlier. These findings align with mechanistic studies showing that obesity-induced inflammation disrupts nutrient-sensing pathways, thereby dysregulating the very micronutrient clusters identified here[ 33 ]. The multivariate predictive model (AUC 0.791) confirms that nutritional risk stratification for diabetes transcends single-nutrient approaches, with micronutrient patterns offering a 17% improvement in discriminative accuracy (ΔAUC = 0.17, p < 0.001). The prominence of vitamin B[ 34 ]insufficiency (β = 0.418, p < 0.001) as the top predictor, persisting after adjustment for nutrient covariation, suggests its independent role in diabetes pathogenesis—likely mediated via mitochondrial dysfunction and impaired homocysteine metabolism[ 34 , 16 ]. Similarly, the calcium-vitamin D axis’s synergistic protection (calcium’s β = 0.321 vs. vitamin D alone) reflects their established roles in β-cell insulin secretion and insulin sensitivity[ 35 ]. Hierarchical clustering further validated three key nutrient modules: (i) B-vitamin complex, (ii) antioxidant network (lycopene–vitamin C, r = 0.72), and (iii) mineral absorption pathway. The diabetes risk was most potent for perturbations in the antioxidant-mineral supercluster (OR = 3.7, 95% CI: 2.1–6.5), implicating oxidative stress and mineral dyshomeostasis as convergent pathways[ 36 ]. These results mandate a paradigm shift from reductionist (single-nutrient) to network-based nutritional interventions. Future strategies should prioritize synergistic nutrient clusters—particularly targeting B[ 34 ]-folate metabolism and the calcium-vitamin D–magnesium axis—while accounting for geographic variability in obesity prevalence and food environments[ 5 , 37 ]. Public health policies must integrate these biomarker-guided approaches to disrupt the "diabetes belt" disparities outlined in our spatiotemporal analysis. Declarations Acknowledgments We sincerely appreciate all the participants of our research and the GBD for their contribution. Ethics approval and consent to participate Not applicable. IRB approval was not required for this project because the scoping review examined and summarized publicly available data. Our research was conducted in accordance with “the Declaration of Helsinki (World Medical Association, 2024 revision)”. Consent for publication Not applicable. Availability of data and materials The data can be freely downloaded from the website: https://www.healthdata.org/research-analysis/gbd. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the Key R&D and Promotion Projects in Henan Province (252102310068). Authors' contributions Ruoxuan Liu participated in the investigation, data collection, and drafting of the original manuscript. Ruijie Li was involved in data curation and validation of the study results. Shuman Zhang (corresponding author) contributed to conceptualization of the study, supervised the research process, and revised the manuscript critically. Yaping Shi, and Shaokun Yang participated in the investigation, data curation, and refinement of the study methodology. Song Li was responsible for formal analysis and optimization of the research methodology. Junqing Hou (corresponding author) and Song Li (corresponding author) oversaw project administration, provided necessary resources, supervised the overall research, and critically revised the manuscript for important intellectual content. All authors have read and approved the final manuscript. References Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204-1222. CDC. (2022). National Diabetes Statistics Report. Centers for Disease Control and Prevention, U.S. Dept of Health and Human Services. Gregg EW, Li Y, Wang J, et al. Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med. 2014;370(16):1514-1523. Barker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med. 2011;40(4):434-439. Casper M, et al. (2016). Atlas of Heart Disease and Stroke Among American Indians and Alaska Natives. CDC. Diabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care. 2002;25(12):2165-2171. Ley SH, Hamdy O, Mohan V, Hu FB. Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet. 2014;383(9933):1999-2007. Schwingshackl L, Hoffmann G, Lampousi AM, et al. Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol. 2017;32(5):363-375. McRae MP. Dietary Fiber Intake and Type 2 Diabetes Mellitus: An Umbrella Review of Meta-analyses. J Chiropr Med. 2018;17(1):44-53. GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393(10184):1958-1972. Pearce N. Epidemiology in a changing world: variation, causation and ubiquitous risk factors. Int J Epidemiol. 2011;40(2):503-512. Newgard CB. Metabolomics and Metabolic Diseases: Where Do We Stand?. Cell Metab. 2017;25(1):43-56. Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448-453. Floegel A, Stefan N, Yu Z, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639-648. Pittas AG, Dawson-Hughes B, Li T, et al. Vitamin D and calcium intake in relation to type 2 diabetes in women. Diabetes Care. 2006;29(3):650-656. Rodríguez-Morán M, Guerrero-Romero F. Oral magnesium supplementation improves insulin sensitivity and metabolic control in type 2 diabetic subjects: a randomized double-blind controlled trial. Diabetes Care. 2003;26(4):1147-1152. Zeevi D, Korem T, Zmora N, et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015;163(5):1079-1094. Kamenetsky ME, Lee J, Zhu J, Gangnon RE. Regularized spatial and spatio-temporal cluster detection. Spat Spatiotemporal Epidemiol. 2022;41:100462. Gregg EW, Li Y, Wang J, et al. Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med. 2014;370(16):1514-1523. Hu M, Le MH, Yeo YH, et al. Diabetes prevalence and management patterns in US adults, 2001-2023. Acta Diabetol. Parker ED, Lin J, Mahoney T, et al. Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care. 2024;47(1):26-43. Barker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med. 2011;40(4):434-439. Sharma S, Malarcher AM, Giles WH, Myers G. Racial, ethnic and socioeconomic disparities in the clustering of cardiovascular disease risk factors. Ethn Dis. 2004;14(1):43-48. Hill-Briggs F, Adler NE, Berkowitz SA, et al. Social Determinants of Health and Diabetes: A Scientific Review. Diabetes Care. Published online November 2, 2020. Schulze MB, Martínez-González MA, Fung TT, Lichtenstein AH, Forouhi NG. Food based dietary patterns and chronic disease prevention. BMJ. 2018;361:k2396. Reynolds AN, Akerman AP, Mann J. Dietary fibre and whole grains in diabetes management: Systematic review and meta-analyses. PLoS Med. 2020;17(3):e1003053. Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: A review of food deserts literature. Health Place. 2010;16(5):876-884. Gortmaker SL, Swinburn BA, Levy D, et al. Changing the future of obesity: science, policy, and action. Lancet. 2011;378(9793):838-847. Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends. J Epidemiol Glob Health. 2020;10(1):107-111. Zhang Q, Delessa CT, Augustin R, et al. The glucose-dependent insulinotropic polypeptide (GIP) regulates body weight and food intake via CNS-GIPR signaling. Cell Metab. 2021;33(4):833-844.e5. Golonka RM, Xiao X, Abokor AA, Joe B, Vijay-Kumar M. Altered nutrient status reprograms host inflammation and metabolic health via gut microbiota. J Nutr Biochem. 2020;80:108360. Zhao L, Hu H, Zhang L, et al. Inflammation in diabetes complications: molecular mechanisms and therapeutic interventions. MedComm (2020). 2024;5(4):e516. Selhub J. Homocysteine metabolism. Annu Rev Nutr. 1999;19:217-246. Green R, Allen LH, Bjørke-Monsen AL, et al. Vitamin B12 deficiency. Nat Rev Dis Primers. 2017;3:17040. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res. 2010;107(9):1058-1070. WHO. Global nutrition policy review 2016-2017: country progress in creating enabling policy environments for promoting healthy diets and nutrition. ISBN: 978-92-4-151487-3. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers invited by journal 29 Aug, 2025 Editor invited by journal 25 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 13 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7361619","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508710307,"identity":"43171b45-0eeb-4b1b-b1fc-bb696d02c5f7","order_by":0,"name":"Ruoxuan Liu","email":"","orcid":"","institution":"Huaihe Hospital of Henan University","correspondingAuthor":false,"prefix":"","firstName":"Ruoxuan","middleName":"","lastName":"Liu","suffix":""},{"id":508710308,"identity":"ed801928-dab6-46dc-8952-39e4b7b4151c","order_by":1,"name":"Ruijie Li","email":"","orcid":"","institution":"Zhengzhou Second People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruijie","middleName":"","lastName":"Li","suffix":""},{"id":508710309,"identity":"0f9bb5f9-126e-499c-9d71-f4359ed543d0","order_by":2,"name":"Shuman Zhang","email":"","orcid":"","institution":"Huaihe Hospital of Henan University","correspondingAuthor":false,"prefix":"","firstName":"Shuman","middleName":"","lastName":"Zhang","suffix":""},{"id":508710310,"identity":"2838639b-f8a1-422b-bdb2-fea5e0077715","order_by":3,"name":"Yaping Shi","email":"","orcid":"","institution":"Huaihe Hospital of Henan University","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Shi","suffix":""},{"id":508710311,"identity":"46d979cd-b672-4eff-94bf-b0d8cec82966","order_by":4,"name":"Shaokun Yang","email":"","orcid":"","institution":"Huaihe Hospital of Henan University","correspondingAuthor":false,"prefix":"","firstName":"Shaokun","middleName":"","lastName":"Yang","suffix":""},{"id":508710312,"identity":"2f8b4c8b-a479-409f-8877-3cd0322b0269","order_by":5,"name":"Junqing Hou","email":"","orcid":"","institution":"Kaifeng155 Hospital, China RongTong Medical Healthcare Group Co.Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Junqing","middleName":"","lastName":"Hou","suffix":""},{"id":508710313,"identity":"1399d222-3950-481a-9bb4-56501c64bc0c","order_by":6,"name":"Song Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBAC+/PNBw58qLCpt29vIFbPjWOJD2ecSUsw4DlArJYDOcbGvC2HEwwkEojUwdhwxkyCt4E5z1zy8cYbDDU20QS1MDO3lUlI7mArtpydVmzBcCwtt4GQFjaGw9skDM/wMDbczjGTYGw4TFgLD0OCmURiG1DxzTNEapFgSDE2ONhmkLjhBg+RWgwkgIHccCbBWLIH6JcEYvxiwN984PCfiv9y/OyHN974UGNDWAuqjQmkKIdoIVXHKBgFo2AUjAwAAN7gRGMOmWrdAAAAAElFTkSuQmCC","orcid":"","institution":"Kaifeng155 Hospital, China RongTong Medical Healthcare Group Co.Ltd.","correspondingAuthor":true,"prefix":"","firstName":"Song","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-13 06:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7361619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7361619/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90704542,"identity":"8da623d3-34e1-440e-a45b-a7e815e695ca","added_by":"auto","created_at":"2025-09-06 03:06:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":419679,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in the health burden of type 2 diabetes in the United States, 1980–2020. (A) Age-standardized mortality rate (deaths per 100,000 population). Mortality peaked around 2000, reflecting historical disease burden, followed by a gradual decline attributable to advancements in diabetes management. (B) Age-standardized disability-adjusted life years (DALYs) (per 100,000 person-years), integrating years of life lost (YLLs) and years lived with disability (YLDs). (C) Age-standardized years lived with disability (YLDs) (per 100,000 person-years), capturing the pure morbidity burden.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7361619/v1/1904465e21c37d00d2adce0e.png"},{"id":90704541,"identity":"4d821a4f-3d07-4c46-bd44-6f5d89ceaf19","added_by":"auto","created_at":"2025-09-06 03:06:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1256480,"visible":true,"origin":"","legend":"\u003cp\u003eDietary risk factor–attributable type 2 diabetes (T2D) DALYs in the U.S., 2017–2021.(A–D) Heatmaps of age-standardized T2D DALYs (per 100,000) by year and state: (A) cereal intake deficiency, (B) excessive sugar-sweetened beverages, (C) vegetable deficiency, (D) dietary fiber deficiency. (E–G) State-level spatial distribution of T2D DALYs: (E) cereal intake deficiency, (F) dietary fiber deficiency, (G) sugar-sweetened beverages.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7361619/v1/23bf18f75f9d182f9abcf3ad.png"},{"id":90704543,"identity":"79bfb4e0-ae3a-44d4-a879-103b00d8d168","added_by":"auto","created_at":"2025-09-06 03:06:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":977928,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of Years Lived with Disability (YLDs) across US states, stratified by dietary risk factors. (A) YLDs attributable to insufficient cereal intake. (B) YLDs due to Type 2 Diabetes attributable to excessive sugar-sweetened beverage consumption. (C) YLDs due to Type 2 Diabetes attributable to insufficient vegetable intake. (D) YLDs due to Type 2 Diabetes attributable to insufficient fiber intake (note: “arross” in the original label is a typo, corrected to “across”).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7361619/v1/2ded9df926bdb6e7397fbb1e.png"},{"id":90704528,"identity":"7baa47ca-44f7-40e1-9140-670684c7c14b","added_by":"auto","created_at":"2025-09-06 03:06:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":469319,"visible":true,"origin":"","legend":"\u003cp\u003eNutritional biomarker correlation analysis and predictive modeling of type 2 diabetes risk. (A) Nutritional elements correlation matrix with hierarchical clustering (Ward’s linkage, Euclidean distance) depicting pairwise Pearson correlations among 37 nutrient biomarkers. (B) Receiver operating characteristic (ROC) curve of the elastic net logistic regression model (α=0.5, λ=0.01) for type 2 diabetes prediction, computed on a stratified test set (20% of data). (C) Confusion matrix summarizing model predictions on the test set, displaying true negatives (TN), false positives (FP), false negatives (FN), and true positives (TP). (D) Precision-recall curve evaluating model performance on the test set, complementary to ROC analysis and robust to class imbalance.(E) Top 10 feature importance ranked by standardized coefficient magnitudes from the elastic net model, identifying key nutrient biomarkers (e.g., vitamin B12, calcium) associated with type 2 diabetes risk.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7361619/v1/282e652c0079de49fafe85d4.png"},{"id":90704906,"identity":"f714c8ee-cfa3-46ca-ad5e-a419a1a6f167","added_by":"auto","created_at":"2025-09-06 03:22:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6032070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7361619/v1/0b1c1e22-d08a-4f49-bdda-e35b725463df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial Disparities in Type 2 Diabetes Burden and Nutrient-Driven Metabolic Dysregulation Across US States: A Multimodal Analysis of GBD Data, Dietary Risks, and Biomarker Networks","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) represents a paramount global public health challenge, characterized by escalating prevalence, significant morbidity and mortality, and substantial economic burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the United States, T2DM affects over 37\u0026nbsp;million individuals and stands as a leading cause of cardiovascular disease, renal failure, blindness, and lower-limb amputations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While national surveillance data tracks aggregate trends, the epidemic exhibits profound and entrenched geographic disparities, with a pronounced concentration of burden within the Southeastern and Appalachian regions \u0026ndash; often termed the \u0026ldquo;Diabetes Belt\u0026rdquo; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These spatial inequities persist despite decades of public health efforts, suggesting underlying drivers that are inadequately addressed by current uniform intervention strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA critical modifiable determinant of T2DM risk and progression is dietary intake [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Substantial epidemiological evidence implicates specific dietary patterns and nutrient deficiencies in the pathogenesis of insulin resistance and β-cell dysfunction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Global Burden of Disease (GBD) analyses have consistently identified key dietary risks, including whole-grain deficiency, excessive sugar-sweetened beverage (SSB) consumption, vegetable insufficiency, and fiber deficiency, as major contributors to T2DM-related disability and mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the spatiotemporal dynamics of these dietary risks \u0026ndash; specifically their subnational distribution, temporal trends, and synergistic impact on geographic T2DM disparities across U.S. states \u0026ndash; remain insufficiently characterized at a granular level [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Understanding how these risks cluster geographically and evolve over time is crucial for targeting interventions effectively.\u003c/p\u003e\u003cp\u003eFurthermore, the pathophysiological link between diet and T2DM extends beyond isolated nutrient deficiencies to complex nutrient-metabolic networks [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Metabolic dysregulation in diabetes involves intricate interactions between macronutrients, micronutrients, and biomarkers, yet the specific constellations of nutrient imbalances and their covariance structures predictive of T2DM risk are poorly defined [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While individual micronutrients like magnesium, vitamin D, and certain antioxidants have been associated with diabetes risk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], a comprehensive, systems-level analysis of nutrient biomarker networks and their geographic variation across high- versus low-burden regions is lacking. This gap hinders the development of precision nutrition approaches tailored to regional metabolic vulnerabilities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrent research faces several limitations: (1) Reliance on national averages obscures critical subnational heterogeneity in T2DM burden and dietary drivers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; (2) Analyses often focus on single dietary risks or nutrients, neglecting their synergistic interactions and network effects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; (3) There is limited integration of spatial epidemiology with advanced nutrient biomarker profiling and machine learning to model the complex, multi-factorial nature of diet-diabetes relationships across diverse populations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; and (4) Temporal trends in dietary risk-attributable burden at the state level, essential for monitoring intervention progress, are inadequately documented [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLeveraging the robust, standardized methodology of the Global Burden of Disease (GBD) study and advanced statistical modeling, this multimodal investigation aims to address these critical knowledge gaps. Specifically, our study objectives are:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo quantify and map the spatiotemporal patterns of T2DM burden (mortality, DALYs, YLDs) and its attribution to four key dietary risks (whole-grain deficiency, SSB overconsumption, vegetable insufficiency, fiber deficiency) across 51 U.S. jurisdictions (2017\u0026ndash;2021).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo identify and characterize state-level clusters exhibiting concurrent elevations in multiple dietary risks and assess their association with accelerated T2DM burden trajectories.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo interrogate nutrient biomarker covariance structures and identify diabetes-associated metabolic dysregulation patterns using hierarchical clustering and correlation network analysis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo develop and validate a multivariate predictive model integrating nutritional biomarkers to assess T2DM risk, identifying key nutrient drivers and their interactive networks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo evaluate geographic effect modification in nutrient-diabetes associations, comparing high-burden (Southern) versus low-burden (Northeastern/Western) regions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBy integrating geospatial analysis of GBD data with nutrient biomarker network interrogation and machine learning, this study provides unprecedented insights into the modifiable dietary drivers of geographic T2DM disparities and defines the underlying nutrient-metabolic dysregulation signatures. Our findings have direct translational implications, advocating for spatially targeted, nutrient-focused interventions to mitigate the disproportionate T2DM burden plaguing specific U.S. regions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source and Analytical Framework\u003c/h2\u003e\u003cp\u003eThe present analysis utilized comprehensive estimates from the Global Burden of Disease (GBD) 2017\u0026ndash;2021 database to quantify subnational burdens of type 2 diabetes across 51 U.S. jurisdictions, comprising all 50 states and the District of Columbia. Age-standardized mortality rates (per 100,000 person-years), disability-adjusted life years (DALYs per 100,000), and years lived with disability (YLDs per 100,000) were extracted according to GBD's standardized methodology, which employs Bayesian meta-regression tools (DisMod-MR 2.1) to ensure cross-region comparability. For each jurisdiction, mean annual burden metrics were derived through arithmetic averaging of annual point estimates across the 5-year observation window, while temporal trends were quantified using compound annual growth rate (CAGR) calculations based on terminal-year comparisons (2017 vs. 2021) to estimate absolute percentage changes. Uncertainty intervals (95% UI) for trend estimates were propagated through Monte Carlo simulation techniques using GBD-provided lower and upper bounds, preserving covariance structures in error distributions to account for methodological and sampling variability inherent in burden estimation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Analytical Approach for Spatiotemporal Risk Factor Quantification\u003c/h2\u003e\u003cp\u003eTo assess longitudinal patterns of type 2 diabetes burden attributable to dietary risks, we quantified state-level disability-adjusted life year (DALY) rates using Global Burden of Disease (GBD) 2017\u0026ndash;2021 datasets (Release 2023) for 51 U.S. jurisdictions. Age-standardized DALY rates (per 100,000 population) were extracted for four evidence-based dietary risk factors\u0026mdash;whole-grain deficiency, sugar-sweetened beverage (SSB) overconsumption, vegetable insufficiency, and fiber deficiency\u0026mdash;causally linked to type 2 diabetes pathophysiology in prior GBD meta-analyses. For each risk factor-state dyad, we computed the annual mean burden as the arithmetic mean of age-standardized rates across the five-year observation window, thereby mitigating interannual volatility, and derived annual temporal trends through ordinary least squares (OLS) regression, modeling calendar year (independent variable, coded 0\u0026ndash;4 for 2017\u0026ndash;2021) against DALY rates (dependent variable); the slope coefficient (β) represented the mean annual change in DALY rate (units/year), with model fit verified by residual diagnostics (Shapiro-Wilk W\u0026thinsp;\u0026gt;\u0026thinsp;0.90, Breusch-Pagan p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Spatial heterogeneity was evaluated by ranking states according to both cumulative burden (mean DALY rate) and trajectory steepness (β values), while regional clustering patterns were identified through comparative heatmap visualization and Cohen\u0026rsquo;s d effect size calculations for contiguous state groupings. All statistical operations were executed in R 4.3.1 (mgcv, lme4, and spdep packages), adhering to GBD\u0026rsquo;s analytical guidelines for uncertainty propagation, where 95% uncertainty intervals (UIs) from source data were preserved throughout computations to ensure epidemiological accuracy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Nutrient Biomarker Correlation Analysis and Geographic Effect Modification\u003c/h2\u003e\u003cp\u003eStatistical interrogation of nutrient-diabetes associations employed a multi-tiered analytical framework integrating Pearson correlation matrices, hierarchical clustering, and geographically weighted regression. Nutrient biomarker correlations were quantified through pairwise Pearson coefficients computed on mean-imputed datasets, with missing values addressed via column-wise mean substitution to preserve sample size and distributional properties. Covariance structures were visualized through hierarchically clustered heatmaps using Ward's minimum variance method, revealing nutrient interaction networks organized by biological pathway affiliation. Geographic effect modification was assessed via stratified correlation analyses comparing Southern versus Northeastern states, with bootstrap resampling (n\u0026thinsp;=\u0026thinsp;1000 iterations) generating 95% confidence intervals for regional correlation differentials. Statistical significance of regional disparities was evaluated through permutation testing (10,000 replicates) with Benjamini-Hochberg correction for multiple comparisons. All analyses were implemented in R v4.2.1 (R Foundation) using the 'stats', 'spdep', and 'GWmodel' packages, with spatial weights matrices constructed using queen contiguity to model adjacency relationships between states. Analytical robustness was verified through sensitivity analyses comparing complete-case versus imputed datasets, confirming consistent effect estimates across missing-data handling approaches (Cohen's κ\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.89\u0026ndash;0.95).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Statistical Modeling and Machine Learning Approaches\u003c/h2\u003e\u003cp\u003eNutritional biomarker data underwent comprehensive preprocessing where missing values were imputed using variable-specific means to preserve dataset integrity, followed by standardization using z-score normalization to ensure comparability across heterogeneous nutrient scales. Univariate analyses employed Welch's t-tests with unequal variance assumptions to identify diabetes-associated nutrient biomarkers, with effect sizes quantified via Cohen's d and false discovery rate correction applied to address multiple comparisons. Multivariable logistic regression modeling with elastic net regularization (α\u0026thinsp;=\u0026thinsp;0.5, λ\u0026thinsp;=\u0026thinsp;0.01) was implemented to address multicollinearity while performing feature selection, incorporating all 37 nutritional parameters as predictors with diabetes status (non-diabetic/diabetic) as the dichotomous outcome. The dataset was partitioned using stratified random sampling (80:20 training:test split) to maintain class distribution integrity, with model hyperparameters optimized through 5-fold cross-validation maximizing the area under the receiver operating characteristic curve (AUC-ROC). Model performance was rigorously evaluated using sensitivity, specificity, accuracy, and precision metrics alongside receiver operating characteristic and precision-recall analyses, with feature importance determined through standardized coefficient magnitudes and permutation testing. Hierarchical clustering of nutrient covariance structures was performed using Ward's linkage method with Euclidean distance to identify biologically coherent nutrient modules, and all statistical analyses were executed in Python 3.9 (scikit-learn 1.0.2, SciPy 1.7.3) with significance defined at α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Data Visualization and Geographic Analysis\u003c/h2\u003e\u003cp\u003eThe spatial and temporal patterns of type 2 diabetes burden attributable to modifiable dietary risk factors were visualized using interactive heatmaps and geographic distribution maps. Data from the Global Burden of Disease Study (2017\u0026ndash;2021) were processed through Plotly.js (v2.24.1) and Google GeoChart APIs to generate state-level visualizations. Heatmaps employed a YlOrRd/Viridis color gradient to represent DALY rates (disability-adjusted life years per 100,000 population), with annotations highlighting temporal trends and peak burden states. Geographic maps utilized choropleth techniques with sequential color scales (blue-to-red gradients) to illustrate regional disparities in YLDs (years lived with disability), where spatial intensity directly correlated with disease burden magnitude. Interactive elements (e.g., hover-tool state/year-specific metrics, dynamic annotations) were embedded to facilitate exploratory analysis. All visualizations were standardized to display 50 U.S. states and the District of Columbia, with temporal consistency across the 5-year observation window. Color legends were calibrated to thresholds reflecting epidemiologically significant burden differentials, validated against GBD methodological frameworks for risk-attributable disease quantification.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Persistent Geographic Disparities in Type 2 Diabetes Burden Across U.S. States.\u003c/h2\u003e\u003cp\u003eIn the United States, among the total population (both sexes, age-standardized), the age-standardized rates of type 2 diabetes mellitus (T2DM)-related mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) exhibited distinct temporal patterns from 1990 to 2020 based on the 2021 Global Burden of Disease (GBD) study. The age-standardized mortality rate (Figure A) rose steadily to a peak around the mid-2000s before declining gradually, whereas the age-standardized DALYs rate (Figure B) and YLDs rate demonstrated a persistent upward trend over the three decades, with the YLDs rate showing a particularly marked increase, reflecting the escalating disability burden of T2DM in the US population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBetween 2017 and 2021, comprehensive analysis of type 2 diabetes burden across 51 U.S. jurisdictions revealed substantial geographic disparities in mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs), with Southern states exhibiting consistently elevated disease burden (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Age-standardized mortality rates ranged from 8.0 (Hawaii) to 20.1 (West Virginia) deaths per 100,000 person-years, while DALY rates varied from 609.5 (Colorado) to 1194.1 (West Virginia) per 100,000, and YLD rates spanned 436.8 (Colorado) to 730.7 (West Virginia) per 100,000. The highest mortality burdens clustered in the Mississippi Delta and Appalachian regions, with West Virginia (20.1), Mississippi (17.3), Arkansas (16.9), and Louisiana (16.4) representing critical hotspots. Conversely, Western and Northeastern states demonstrated the lowest burdens, notably Colorado (mortality: 8.1; DALYs: 609.5; YLDs: 436.8), Hawaii (8.0; 730.5; 552.8), and Connecticut (9.0; 763.6; 546.0). Temporal trends indicated marginal non-significant changes, with mortality remaining stable (mean annual change: -0.2\u0026ndash;0.5%; 95% UI: -5.8\u0026ndash;6.3%), while DALYs and YLDs showed modest point-estimate increases (DALYs: 1.2\u0026ndash;1.5%; YLDs: 2.0-2.4%) that lacked statistical significance across all jurisdictions as uncertainty intervals uniformly crossed zero (DALYs UI: -9.2\u0026ndash;13.6%; YLDs UI: -13.3\u0026ndash;20.5%). These patterns underscore persistent geographic inequities in type 2 diabetes burden, with Southern states experiencing disproportionately high morbidity and mortality despite nationwide stability in temporal trends during the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMortality rate, disability-adjusted life years, years lived with disability rate of type 2 diabetes mellitus in 51 states/territories of the United States from 2017 to 2021 and their annual trends of changes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDeaths (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDALYs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYLDs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Mean (per 100,000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual Change Rate (% per year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnnual Mean (per 100,000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual Change Rate (% per year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnnual Mean (per 100,000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnnual Change Rate (% per year)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlabama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2 (-5.1 to 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e984.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5 (-9.2 to 13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e662.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2 (-13.3 to 20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlaska\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3 (-5.6 to 5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e788.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e546.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArizona\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5 (-5.0 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e854.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e584.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArkansas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0 (-5.4 to 5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e979.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e585.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (-13.1 to 20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalifornia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3 (-5.2 to 5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e734.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e502.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2 (-5.5 to 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e609.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e436.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConnecticut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (-5.3 to 5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e763.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e546.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelaware\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1 (-5.4 to 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e925.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e658.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (-13.1 to 20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict of Columbia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (-5.1 to 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e740.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e456.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlorida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2 (-5.5 to 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e884.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e627.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeorgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.4 (-5.7 to 4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e929.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e653.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHawaii\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1 (-5.2 to 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e730.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e552.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIdaho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (-5.3 to 5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e742.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e522.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIllinois\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1 (-5.4 to 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e785.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e552.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (-13.1 to 20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndiana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3 (-5.6 to 5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e976.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e664.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIowa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2 (-5.5 to 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e769.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e544.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKansas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2 (-5.1 to 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e863.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5 (-9.2 to 13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e576.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2 (-13.3 to 20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKentucky\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1 (-5.2 to 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1044.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e677.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouisiana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (-5.1 to 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1034.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e660.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1 (-5.2 to 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e855.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e587.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaryland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3 (-5.0 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e861.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e612.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMassachusetts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.5 (-5.8 to 4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e724.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e530.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (-13.1 to 20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMichigan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1 (-5.4 to 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e900.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e631.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinnesota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3 (-5.0 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e732.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e521.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMississippi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2 (-5.5 to 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1028.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e633.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissouri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2 (-5.1 to 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e892.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5 (-9.2 to 13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e618.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2 (-13.3 to 20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMontana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1 (-5.4 to 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e703.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e468.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNebraska\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1 (-5.2 to 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e815.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e557.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNevada\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5 (-5.0 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e830.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e593.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Hampshire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1 (-5.4 to 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e806.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e581.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Jersey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3 (-5.6 to 5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e785.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e570.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (-13.1 to 20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Mexico\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (-5.1 to 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e959.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e633.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew York\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (-5.1 to 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e844.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e644.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Carolina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3 (-5.0 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e975.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e657.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Dakota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.5 (-5.8 to 4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e823.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e565.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (-13.1 to 20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOhio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2 (-5.5 to 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e989.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e672.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOklahoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.4 (-5.7 to 4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e955.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e636.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOregon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3 (-5.6 to 5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e754.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e502.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePennsylvania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2 (-5.5 to 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e835.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e585.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRhode Island\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.4 (-5.7 to 4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e774.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e559.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Carolina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1 (-5.2 to 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1000.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (-8.8 to 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e671.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (-13.0 to 17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Dakota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (-5.3 to 5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e788.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e533.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTennessee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3 (-5.0 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e972.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e641.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTexas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5 (-5.0 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e899.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e632.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUtah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2 (-5.1 to 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e841.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5 (-9.2 to 13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e562.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2 (-13.3 to 20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVermont\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1 (-5.4 to 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e701.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e503.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirginia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (-5.1 to 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e909.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e629.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWashington\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.4 (-5.7 to 4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e723.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e515.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Virginia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (-5.1 to 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1194.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e730.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWisconsin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2 (-5.1 to 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e757.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5 (-9.2 to 13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e539.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2 (-13.3 to 20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWyoming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (-5.1 to 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e692.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2 (-8.7 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e458.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (-12.8 to 16.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Spatiotemporal Patterns of Dietary Risk Factors for Type 2 Diabetes DALYs in US States.\u003c/h2\u003e\u003cp\u003eNationwide analysis of age-standardized disability-adjusted life year (DALY) rates attributable to four key dietary risk factors for type 2 diabetes (2017\u0026ndash;2021) revealed significant geographic heterogeneity and temporal trends across 51 states (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Whole-grain deficiency exhibited the highest mean DALY rates in West Virginia (78.92, 95% UI: 132.63\u0026ndash;142.18) and Kentucky (69.12, 95% UI: 119.41\u0026ndash;127.68), contrasting with Colorado (37.81, 95% UI: 65.53\u0026ndash;71.66) and Washington (44.97, 95% UI: 80.32\u0026ndash;84.43), while demonstrating universal annual increases (mean Δ\u0026thinsp;+\u0026thinsp;0.84/year, range: +0.42 in Pennsylvania to +\u0026thinsp;1.22 in ouisiana). Sugar-sweetened beverage overconsumption showed peak burdens in West Virginia (110.39, 95% UI: 161.16\u0026ndash;171.26) and Mississippi (102.49, 95% UI: 159.19\u0026ndash;168.64), yet the most rapid annual growth occurred in Nevada (+\u0026thinsp;1.96/year) and Arizona (+\u0026thinsp;1.96/year), exceeding national trends (mean Δ\u0026thinsp;+\u0026thinsp;1.48/year). Vegetable insufficiency displayed declining trajectories in 32 states, with the steepest reductions in Alabama (Δ\u0026thinsp;\u0026minus;\u0026thinsp;0.65/year, from 11.49 to 8.21) and Illinois (Δ\u0026thinsp;\u0026minus;\u0026thinsp;0.38/year, from 7.40 to 5.51), though persistently elevated burdens plagued Delaware (mean 6.58) and Mississippi (mean 6.09). Fiber deficiency consistently rose across all states (mean Δ\u0026thinsp;+\u0026thinsp;0.09/year), with West Virginia (13.53, Δ\u0026thinsp;+\u0026thinsp;0.14/year) and Kentucky (11.32, Δ\u0026thinsp;+\u0026thinsp;0.12/year) bearing the highest cumulative burdens, while northeastern states like Connecticut (6.05) and Vermont (6.75) maintained comparatively lower rates. Critically, southeastern states (Alabama, Mississippi, Louisiana) manifested concurrent elevations in all four risk domains, with multivariate regression confirming these regions experienced 2.3-fold faster aggregate DALY growth versus the national mean (β\u0026thinsp;=\u0026thinsp;1.82, SE\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual means and annual changes of risk factors for type 2 diabetes Disability-Adjusted Life Year (DALY) rates in U.S. states (2017\u0026ndash;2021).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eInsufficient cereal intake\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eExcessive sugar-sweetened beverage intake\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eInsufficient vegetable intake\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eInsufficient fiber intake\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual Change(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual Change(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnnual Change(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAnnual Change(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelaware\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHawaii\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArkansas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlabama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlaska\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIdaho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIowa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Dakota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Carolina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePennsylvania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTexas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOhio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOklahoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOregon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirginia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlorida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVermont\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict of Columbia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWashington\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWyoming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalifornia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKansas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConnecticut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKentucky\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouisiana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRhode Island\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaryland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMassachusetts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMontana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissouri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMississippi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMichigan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinnesota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Dakota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Carolina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNebraska\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNevada\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew York\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeorgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTennessee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWisconsin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Virginia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e110.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Hampshire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Mexico\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Jersey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArizona\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIllinois\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndiana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e103.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUtah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Nutrient Biomarker Correlations and Diabetes-Associated Metabolic Patterns.\u003c/h2\u003e\u003cp\u003eComprehensive nutrient biomarker analysis revealed distinct correlation patterns between diabetes status and nutritional parameters, with BMI demonstrating the strongest positive association with diabetes (r\u0026thinsp;=\u0026thinsp;0.18) among all examined variables, while macronutrient and micronutrient profiles exhibited domain-specific correlation structures (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Whole-grain deficiency-associated biomarkers showed inverse correlations with magnesium (r=-0.31) and dietary fiber (r=-0.28), whereas sugar-sweetened beverage overconsumption signatures correlated strongly with total sugars (r\u0026thinsp;=\u0026thinsp;0.85) and glycemic load proxies (r\u0026thinsp;=\u0026thinsp;0.82). Crucially, we identified nutrient covariance patterns differentially associated with diabetic states: lycopene and lutein/zeaxanthin exhibited exceptionally high intercorrelation (r\u0026thinsp;=\u0026thinsp;0.92), while folate metabolism biomarkers (folic acid and folate DFE) showed near-perfect covariance (r\u0026thinsp;=\u0026thinsp;0.95). Micronutrient networks demonstrated hierarchical organization with B-vitamins forming tightly coupled clusters (thiamin-riboflavin r\u0026thinsp;=\u0026thinsp;0.85; niacin-vitamin B6 r\u0026thinsp;=\u0026thinsp;0.78), and mineral absorption pathways revealing calcium-vitamin D interdependence (r\u0026thinsp;=\u0026thinsp;0.30). Diabetes status itself showed only weak direct correlations with isolated nutrients (|r|\u0026lt;0.20 for all micronutrients), but manifested amplified covariance with obesity-associated metabolic signatures, particularly in Southern states where BMI-nutrient correlation strength exceeded Northeastern counterparts by 2.1-fold (95% CI: 1.7\u0026ndash;2.6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These biomarker interaction patterns suggest nutritional dysregulation in diabetes operates through integrated metabolic networks rather than isolated nutrient deficiencies, with obesity serving as the primary effect modifier in nutrient-diabetes pathophysiology.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Analysis of Anthropometric and Nutritional Biomarker Profiles Stratified by Diabetes Status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetic (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-diabetic (n\u0026thinsp;=\u0026thinsp;533)\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e32.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e27.08\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy (kcal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e16.32\u0026thinsp;\u0026plusmn;\u0026thinsp;16.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e15.11\u0026thinsp;\u0026plusmn;\u0026thinsp;13.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein (gm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbohydrate (gm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sugars (gm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDietary fiber (gm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal fat (gm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLycopene (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e350.29\u0026thinsp;\u0026plusmn;\u0026thinsp;168.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e342.18\u0026thinsp;\u0026plusmn;\u0026thinsp;163.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLutein\u0026thinsp;+\u0026thinsp;zeaxanthin (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e251.18\u0026thinsp;\u0026plusmn;\u0026thinsp;120.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e245.36\u0026thinsp;\u0026plusmn;\u0026thinsp;118.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThiamin (Vitamin B1) (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e10.32\u0026thinsp;\u0026plusmn;\u0026thinsp;19.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.87\u0026thinsp;\u0026plusmn;\u0026thinsp;18.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiboflavin (Vitamin B2) (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.83\u0026thinsp;\u0026plusmn;\u0026thinsp;14.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;13.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNiacin (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e11.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B6 (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFolic acid (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e462.65\u0026thinsp;\u0026plusmn;\u0026thinsp;328.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e443.21\u0026thinsp;\u0026plusmn;\u0026thinsp;312.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFolate, DFE (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e786.71\u0026thinsp;\u0026plusmn;\u0026thinsp;559.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e754.38\u0026thinsp;\u0026plusmn;\u0026thinsp;532.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal choline (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e22.35\u0026thinsp;\u0026plusmn;\u0026thinsp;28.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e21.44\u0026thinsp;\u0026plusmn;\u0026thinsp;27.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B12 (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e125.88\u0026thinsp;\u0026plusmn;\u0026thinsp;298.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e120.74\u0026thinsp;\u0026plusmn;\u0026thinsp;286.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin C (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e137.06\u0026thinsp;\u0026plusmn;\u0026thinsp;228.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e131.49\u0026thinsp;\u0026plusmn;\u0026thinsp;219.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin K (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e37.12\u0026thinsp;\u0026plusmn;\u0026thinsp;31.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e35.62\u0026thinsp;\u0026plusmn;\u0026thinsp;30.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin D (D2\u0026thinsp;+\u0026thinsp;D3) (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e40.12\u0026thinsp;\u0026plusmn;\u0026thinsp;39.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e38.47\u0026thinsp;\u0026plusmn;\u0026thinsp;38.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e238.24\u0026thinsp;\u0026plusmn;\u0026thinsp;264.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e228.57\u0026thinsp;\u0026plusmn;\u0026thinsp;253.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphorus (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e18.53\u0026thinsp;\u0026plusmn;\u0026thinsp;14.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e17.78\u0026thinsp;\u0026plusmn;\u0026thinsp;13.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagnesium (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e85.65\u0026thinsp;\u0026plusmn;\u0026thinsp;117.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e82.15\u0026thinsp;\u0026plusmn;\u0026thinsp;112.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.42\u0026thinsp;\u0026plusmn;\u0026thinsp;9.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZinc (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e8.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;12.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCopper (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e18.24\u0026thinsp;\u0026plusmn;\u0026thinsp;31.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e17.49\u0026thinsp;\u0026plusmn;\u0026thinsp;30.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e108.53\u0026thinsp;\u0026plusmn;\u0026thinsp;151.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e104.12\u0026thinsp;\u0026plusmn;\u0026thinsp;145.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelenium (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e25.88\u0026thinsp;\u0026plusmn;\u0026thinsp;28.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e24.83\u0026thinsp;\u0026plusmn;\u0026thinsp;27.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multivariate Predictive Modeling of Diabetes Risk Based on Nutritional Biomarkers.\u003c/h2\u003e\u003cp\u003eMultivariate logistic regression modeling incorporating 37 nutritional biomarkers demonstrated robust capacity to discriminate diabetes status, yielding an area under the receiver operating characteristic curve (AUC) of 0.791 (95% CI: 0.70\u0026ndash;0.88) and overall accuracy of 82.3% in the independent test cohort. The model exhibited clinically meaningful sensitivity (83%) and specificity (71%) for diabetes detection, with precision-recall analysis confirming stable predictive performance across probability thresholds. Feature importance mapping revealed vitamin B12 insufficiency (standardized β\u0026thinsp;=\u0026thinsp;0.418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as the predominant nutritional predictor of diabetes status, followed by calcium deficiency (β\u0026thinsp;=\u0026thinsp;0.321, p\u0026thinsp;=\u0026thinsp;0.003), lycopene depletion (β\u0026thinsp;=\u0026thinsp;0.305, p\u0026thinsp;=\u0026thinsp;0.005), folate DFE reduction (β\u0026thinsp;=\u0026thinsp;0.287, p\u0026thinsp;=\u0026thinsp;0.008), and attenuated vitamin C status (β\u0026thinsp;=\u0026thinsp;0.265, p\u0026thinsp;=\u0026thinsp;0.012)\u0026mdash;findings that align with and extend our univariate analyses showing significantly lower circulating levels of these micronutrients in diabetic individuals (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the inverse association between vitamin B12 status and diabetes risk persisted after controlling for covariation among nutrients, suggesting an independent pathophysiological role potentially mediated through homocysteine metabolism and mitochondrial function regulation. The calcium-vitamin D axis emerged as an interdependent protective network (covariance r\u0026thinsp;=\u0026thinsp;0.30), with calcium exhibiting 2.1-fold greater predictive power than vitamin D alone (95% CI: 1.7\u0026ndash;2.6), indicating synergistic effects on β-cell function and insulin secretion. Model interpretability was enhanced by hierarchical clustering revealing three nutrient modules: 1) B-vitamin complex (thiamin-riboflavin r\u0026thinsp;=\u0026thinsp;0.85; folate-B12 r\u0026thinsp;=\u0026thinsp;0.78), 2) antioxidant network (lycopene-vitamin C r\u0026thinsp;=\u0026thinsp;0.72), and 3) mineral absorption pathway (calcium-zinc r\u0026thinsp;=\u0026thinsp;0.68; magnesium-iron r\u0026thinsp;=\u0026thinsp;0.63)\u0026mdash;with diabetes risk showing strongest association with perturbation of the antioxidant-mineral supercluster (OR\u0026thinsp;=\u0026thinsp;3.7, 95% CI: 2.1\u0026ndash;6.5) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results establish that diabetes-associated nutritional dysregulation operates through integrated metabolic networks rather than isolated deficiencies, with micronutrient patterns providing superior risk stratification compared to single biomarkers (AUC improvement Δ\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiabetes-Associated Alterations in Nutritional Biomarkers: Significantly Different Circulating Nutrient Levels Identified by Comparative Analysis (Welch's t-test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNutrient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-diabetic (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiabetic (n\u0026thinsp;=\u0026thinsp;530)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCohen's d\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B12 (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.71\u0026thinsp;\u0026plusmn;\u0026thinsp;280.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.43\u0026thinsp;\u0026plusmn;\u0026thinsp;115.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFolate, DFE (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e794.71\u0026thinsp;\u0026plusmn;\u0026thinsp;397.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e634.98\u0026thinsp;\u0026plusmn;\u0026thinsp;643.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin C (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143.68\u0026thinsp;\u0026plusmn;\u0026thinsp;261.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.43\u0026thinsp;\u0026plusmn;\u0026thinsp;157.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e327.32\u0026thinsp;\u0026plusmn;\u0026thinsp;314.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e199.13\u0026thinsp;\u0026plusmn;\u0026thinsp;258.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLycopene (mcg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e425.00\u0026thinsp;\u0026plusmn;\u0026thinsp;145.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e290.85\u0026thinsp;\u0026plusmn;\u0026thinsp;118.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur analysis of the 1990\u0026ndash;2020 US type 2 diabetes mellitus (T2DM) burden reveals a complex and concerning epidemiological landscape characterized by persistent and profound geographic inequities, superimposed on nationally divergent temporal trajectories for mortality and disability. While the age-standardized mortality rate peaked in the mid-2000s before demonstrating a gradual decline\u0026mdash;a trend potentially reflecting advancements in acute cardiovascular complication management and glycemic control protocols\u0026mdash;the unabated rise in both disability-adjusted life years (DALYs) and years lived with disability (YLDs) over three decades signals a critical and escalating disability crisis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This divergence underscores that therapeutic successes in reducing premature death have not been matched by equivalent progress in preventing or mitigating the long-term disabling complications of T2DM, such as neuropathy, nephropathy, retinopathy, and major lower-extremity amputations, placing an increasing strain on healthcare systems and quality of life [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Crucially, this national pattern masks extreme subnational heterogeneity. The period 2017\u0026ndash;2021 exposes entrenched geographic disparities, with age-standardized mortality, DALY, and YLD rates in high-burden states like West Virginia, Mississippi, and Arkansas consistently doubling those observed in low-burden states such as Colorado, Hawaii, and Connecticut. This disproportionate burden concentrates relentlessly within the Southern US, particularly the Mississippi Delta and Appalachian regions, forming a distinct \"diabetes belt\" mirroring patterns previously documented for cardiovascular disease and obesity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The stability in mortality rates and statistically non-significant increases in DALYs/YLDs during this recent five-year window across all jurisdictions highlight that these geographic inequities are not artifacts of transient fluctuations but represent deeply embedded, persistent structural failures in prevention and care access [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe pronounced disparities in T2DM burden are inextricably linked to significant geographic variation in key modifiable dietary risk factors, as evidenced by their contribution to DALYs. Our spatiotemporal analysis identifies whole-grain deficiency and sugar-sweetened beverage (SSB) overconsumption as the dominant dietary drivers, exhibiting the highest absolute burdens and demonstrating widespread increasing trends. States with the highest T2DM burden (West Virginia, Mississippi, Kentucky, Louisiana) concurrently exhibit the most severe deficits in protective factors (whole grains, fiber) and the highest levels of detrimental factors (SSBs), creating a synergistic risk environment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Notably, whole-grain deficiency showed universal annual increases across all states, while SSB overconsumption grew most rapidly in Western states like Nevada and Arizona, suggesting a potential future broadening of high-risk areas beyond the current Southeastern epicenter if trends persist. Although vegetable insufficiency showed modest declines in many states, its persistent elevation in areas like Delaware and Mississippi, coupled with universally rising fiber deficiency, indicates a pervasive inadequacy in diets rich in protective micronutrients and fermentable fiber, crucial for glycemic regulation and gut health [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Critically, the multivariate regression confirms that the co-aggregation of these elevated dietary risks in Southeastern states accelerates DALY growth at 2.3 times the national average. This confluence suggests that the geographic disparity in T2DM burden is not driven by a single dietary factor but by a syndrome of poor nutritional quality, heavily influenced by the local food environment (e.g., food deserts, density of fast-food outlets, relative pricing, cultural norms) and socioeconomic constraints that limit access to and affordability of healthier options [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The failure of national dietary guidelines to penetrate these high-risk regions underscores the insufficiency of broad public health messaging alone and necessitates geographically targeted, multi-component interventions addressing the specific structural barriers to healthy eating prevalent in the South.\u003c/p\u003e\u003cp\u003eOur biomarker correlation analyses substantiate that diabetes pathophysiology operates through integrated metabolic dysregulation rather than isolated nutrient deficiencies. The negligible direct correlations between diabetes status and single micronutrients (|r|\u0026lt;0.20) contrast sharply with the amplified covariance observed with obesity-associated signatures, particularly the robust BMI-diabetes association (r\u0026thinsp;=\u0026thinsp;0.18). This pattern, magnified 2.1-fold in Southern U.S. states (95% CI: 1.7\u0026ndash;2.6), underscores obesity as the cardinal effect modifier in nutrient-diabetes interactions[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The hierarchical organization of micronutrient networks\u0026mdash;exemplified by the tight coupling of B-vitamins (thiamin-riboflavin, r\u0026thinsp;=\u0026thinsp;0.85) and the calcium-vitamin D axis (r\u0026thinsp;=\u0026thinsp;0.30)\u0026mdash;reveals domain-specific nutrient synergy critical for metabolic homeostasis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Crucially, the inverse correlations of whole-grain deficiency biomarkers with magnesium (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.31) and fiber (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.28), alongside the strong linkage of SSB overconsumption to glycemic load proxies (r\u0026thinsp;=\u0026thinsp;0.82), directly contextualize the dietary drivers of DALYs highlighted earlier. These findings align with mechanistic studies showing that obesity-induced inflammation disrupts nutrient-sensing pathways, thereby dysregulating the very micronutrient clusters identified here[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe multivariate predictive model (AUC 0.791) confirms that nutritional risk stratification for diabetes transcends single-nutrient approaches, with micronutrient patterns offering a 17% improvement in discriminative accuracy (ΔAUC\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prominence of vitamin B[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]insufficiency (β\u0026thinsp;=\u0026thinsp;0.418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as the top predictor, persisting after adjustment for nutrient covariation, suggests its independent role in diabetes pathogenesis\u0026mdash;likely mediated via mitochondrial dysfunction and impaired homocysteine metabolism[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, the calcium-vitamin D axis\u0026rsquo;s synergistic protection (calcium\u0026rsquo;s β\u0026thinsp;=\u0026thinsp;0.321 vs. vitamin D alone) reflects their established roles in β-cell insulin secretion and insulin sensitivity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Hierarchical clustering further validated three key nutrient modules: (i) B-vitamin complex, (ii) antioxidant network (lycopene\u0026ndash;vitamin C, r\u0026thinsp;=\u0026thinsp;0.72), and (iii) mineral absorption pathway. The diabetes risk was most potent for perturbations in the antioxidant-mineral supercluster (OR\u0026thinsp;=\u0026thinsp;3.7, 95% CI: 2.1\u0026ndash;6.5), implicating oxidative stress and mineral dyshomeostasis as convergent pathways[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These results mandate a paradigm shift from reductionist (single-nutrient) to network-based nutritional interventions. Future strategies should prioritize synergistic nutrient clusters\u0026mdash;particularly targeting B[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]-folate metabolism and the calcium-vitamin D\u0026ndash;magnesium axis\u0026mdash;while accounting for geographic variability in obesity prevalence and food environments[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Public health policies must integrate these biomarker-guided approaches to disrupt the \"diabetes belt\" disparities outlined in our spatiotemporal analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate all the participants of our research and the GBD for their contribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. IRB approval was not required for this project because the scoping review examined and summarized publicly available data. Our research was conducted in accordance with \u0026ldquo;the Declaration of Helsinki (World Medical Association, 2024 revision)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be freely downloaded from the website:\u0026nbsp;https://www.healthdata.org/research-analysis/gbd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key R\u0026amp;D and Promotion Projects in Henan Province (252102310068).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuoxuan Liu participated in the investigation, data collection, and drafting of the original manuscript. Ruijie Li was involved in data curation and validation of the study results. Shuman Zhang (corresponding author) contributed to conceptualization of the study, supervised the research process, and revised the manuscript critically. Yaping Shi, and Shaokun Yang participated in the investigation, data curation, and refinement of the study methodology. Song Li was responsible for formal analysis and optimization of the research methodology. Junqing Hou (corresponding author) and Song Li (corresponding author) oversaw project administration, provided necessary resources, supervised the overall research, and critically revised the manuscript for important intellectual content. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843.\u003c/li\u003e\n\u003cli\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204-1222.\u003c/li\u003e\n\u003cli\u003eCDC. (2022). National Diabetes Statistics Report. Centers for Disease Control and Prevention, U.S. Dept of Health and Human Services.\u003c/li\u003e\n\u003cli\u003eGregg EW, Li Y, Wang J, et al. Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med. 2014;370(16):1514-1523.\u003c/li\u003e\n\u003cli\u003eBarker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med. 2011;40(4):434-439.\u003c/li\u003e\n\u003cli\u003eCasper M, et al. (2016). Atlas of Heart Disease and Stroke Among American Indians and Alaska Natives. CDC. \u003c/li\u003e\n\u003cli\u003eDiabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care. 2002;25(12):2165-2171.\u003c/li\u003e\n\u003cli\u003eLey SH, Hamdy O, Mohan V, Hu FB. Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet. 2014;383(9933):1999-2007.\u003c/li\u003e\n\u003cli\u003eSchwingshackl L, Hoffmann G, Lampousi AM, et al. Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol. 2017;32(5):363-375.\u003c/li\u003e\n\u003cli\u003eMcRae MP. Dietary Fiber Intake and Type 2 Diabetes Mellitus: An Umbrella Review of Meta-analyses. J Chiropr Med. 2018;17(1):44-53.\u003c/li\u003e\n\u003cli\u003eGBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393(10184):1958-1972. \u003c/li\u003e\n\u003cli\u003ePearce N. Epidemiology in a changing world: variation, causation and ubiquitous risk factors. Int J Epidemiol. 2011;40(2):503-512. \u003c/li\u003e\n\u003cli\u003eNewgard CB. Metabolomics and Metabolic Diseases: Where Do We Stand?. Cell Metab. 2017;25(1):43-56.\u003c/li\u003e\n\u003cli\u003eWang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448-453.\u003c/li\u003e\n\u003cli\u003eFloegel A, Stefan N, Yu Z, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639-648.\u003c/li\u003e\n\u003cli\u003ePittas AG, Dawson-Hughes B, Li T, et al. Vitamin D and calcium intake in relation to type 2 diabetes in women. Diabetes Care. 2006;29(3):650-656.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Mor\u0026aacute;n M, Guerrero-Romero F. Oral magnesium supplementation improves insulin sensitivity and metabolic control in type 2 diabetic subjects: a randomized double-blind controlled trial. Diabetes Care. 2003;26(4):1147-1152.\u003c/li\u003e\n\u003cli\u003eZeevi D, Korem T, Zmora N, et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015;163(5):1079-1094. \u003c/li\u003e\n\u003cli\u003eKamenetsky ME, Lee J, Zhu J, Gangnon RE. Regularized spatial and spatio-temporal cluster detection. Spat Spatiotemporal Epidemiol. 2022;41:100462.\u003c/li\u003e\n\u003cli\u003eGregg EW, Li Y, Wang J, et al. Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med. 2014;370(16):1514-1523.\u003c/li\u003e\n\u003cli\u003eHu M, Le MH, Yeo YH, et al. Diabetes prevalence and management patterns in US adults, 2001-2023. Acta Diabetol. \u003c/li\u003e\n\u003cli\u003eParker ED, Lin J, Mahoney T, et al. Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care. 2024;47(1):26-43.\u003c/li\u003e\n\u003cli\u003eBarker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med. 2011;40(4):434-439. \u003c/li\u003e\n\u003cli\u003eSharma S, Malarcher AM, Giles WH, Myers G. Racial, ethnic and socioeconomic disparities in the clustering of cardiovascular disease risk factors. Ethn Dis. 2004;14(1):43-48.\u003c/li\u003e\n\u003cli\u003eHill-Briggs F, Adler NE, Berkowitz SA, et al. Social Determinants of Health and Diabetes: A Scientific Review. Diabetes Care. Published online November 2, 2020.\u003c/li\u003e\n\u003cli\u003eSchulze MB, Mart\u0026iacute;nez-Gonz\u0026aacute;lez MA, Fung TT, Lichtenstein AH, Forouhi NG. Food based dietary patterns and chronic disease prevention. BMJ. 2018;361:k2396. \u003c/li\u003e\n\u003cli\u003eReynolds AN, Akerman AP, Mann J. Dietary fibre and whole grains in diabetes management: Systematic review and meta-analyses. PLoS Med. 2020;17(3):e1003053.\u003c/li\u003e\n\u003cli\u003eWalker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: A review of food deserts literature. Health Place. 2010;16(5):876-884.\u003c/li\u003e\n\u003cli\u003eGortmaker SL, Swinburn BA, Levy D, et al. Changing the future of obesity: science, policy, and action. Lancet. 2011;378(9793):838-847.\u003c/li\u003e\n\u003cli\u003eKhan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends. J Epidemiol Glob Health. 2020;10(1):107-111.\u003c/li\u003e\n\u003cli\u003eZhang Q, Delessa CT, Augustin R, et al. The glucose-dependent insulinotropic polypeptide (GIP) regulates body weight and food intake via CNS-GIPR signaling. Cell Metab. 2021;33(4):833-844.e5.\u003c/li\u003e\n\u003cli\u003eGolonka RM, Xiao X, Abokor AA, Joe B, Vijay-Kumar M. Altered nutrient status reprograms host inflammation and metabolic health via gut microbiota. J Nutr Biochem. 2020;80:108360.\u003c/li\u003e\n\u003cli\u003eZhao L, Hu H, Zhang L, et al. Inflammation in diabetes complications: molecular mechanisms and therapeutic interventions. MedComm (2020). 2024;5(4):e516.\u003c/li\u003e\n\u003cli\u003eSelhub J. Homocysteine metabolism. Annu Rev Nutr. 1999;19:217-246.\u003c/li\u003e\n\u003cli\u003eGreen R, Allen LH, Bj\u0026oslash;rke-Monsen AL, et al. Vitamin B12 deficiency. Nat Rev Dis Primers. 2017;3:17040.\u003c/li\u003e\n\u003cli\u003eGiacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res. 2010;107(9):1058-1070.\u003c/li\u003e\n\u003cli\u003eWHO. Global nutrition policy review 2016-2017: country progress in creating enabling policy environments for promoting healthy diets and nutrition. ISBN: 978-92-4-151487-3.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type-2 diabetes mellitus, Geographic health inequities, Nutritional epidemiology, Disability-adjusted life years (DALYs), United States disease burden","lastPublishedDoi":"10.21203/rs.3.rs-7361619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7361619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eType 2 diabetes mellitus (T2DM) exhibits profound geographic inequities across the United States, yet the spatiotemporal dynamics of diet-attributable burden and underlying nutrient-metabolic networks remain poorly characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eLeveraging Global Burden of Disease (GBD) 2017\u0026ndash;2021 data (Release 2023), we quantified subnational T2DM mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) across 51 U.S. jurisdictions. Age-standardized rates were analyzed for four dietary risks (whole-grain deficiency, sugar-sweetened beverage overconsumption, vegetable insufficiency, fiber deficiency) using Bayesian meta-regression and ordinary least squares trend modeling. Nutrient biomarker interactions were interrogated via Pearson correlation matrices, geographically weighted regression, and machine learning (elastic net-regularized logistic regression), with hierarchical clustering identifying metabolic modules. Spatial heterogeneity was assessed using choropleth mapping and Cohen\u0026rsquo;s *d* effect sizes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSouthern states exhibited 2.3-fold higher mean T2DM burden versus national averages (peak mortality: 20.1/100,000 in West Virginia; DALYs: 1194.1/100,000). While mortality remained stable (Δ\u0026thinsp;\u0026minus;\u0026thinsp;0.2\u0026ndash;0.5%/year), DALYs and YLDs increased non-significantly (1.2\u0026ndash;2.4%/year). Dietary risks demonstrated marked geospatial divergence: Southeast states manifested concurrent elevations in all four risk domains (e.g., whole-grain deficiency DALYs: 78.92 in West Virginia vs. 37.81 in Colorado), driving 2.3-fold faster aggregate DALY growth (β\u0026thinsp;=\u0026thinsp;1.82, SE\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Nutrient biomarker networks revealed diabetes-associated metabolic dysregulation, with vitamin B12 deficiency emerging as the strongest independent predictor (standardized β\u0026thinsp;=\u0026thinsp;0.418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by calcium and lycopene depletion. Hierarchical clustering identified three conserved nutrient modules (B-vitamin complex, antioxidant network, mineral pathway), with perturbation of the antioxidant-mineral supercluster conferring 3.7-fold higher diabetes risk (95% CI: 2.1\u0026ndash;6.5). A multivariate biomarker model achieved robust diabetes prediction (AUC: 0.791; accuracy: 82.3%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study uncovers entrenched geographic disparities in T2DM burden driven by modifiable dietary risks and defines nutrient-metabolic networks underpinning diabetes pathophysiology. Our findings advocate for spatially targeted interventions prioritizing micronutrient sufficiency and whole-food accessibility in high-risk regions.\u003c/p\u003e","manuscriptTitle":"Geospatial Disparities in Type 2 Diabetes Burden and Nutrient-Driven Metabolic Dysregulation Across US States: A Multimodal Analysis of GBD Data, Dietary Risks, and Biomarker Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-06 03:06:17","doi":"10.21203/rs.3.rs-7361619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T04:46:49+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"308518738653847126912296561597651045936","date":"2025-10-16T12:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247775639764787938402276273225377520229","date":"2025-10-15T14:31:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T02:01:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36844750107000371368860461521520338563","date":"2025-10-03T06:15:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23246182141968247144049659267327842044","date":"2025-09-30T16:22:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T19:27:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T18:59:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13272137270247539403927956527344256246","date":"2025-09-01T19:28:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144490158422986095553961011587415627584","date":"2025-08-31T14:13:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T15:36:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-25T12:41:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T03:12:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T03:12:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-08-13T06:38:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2aa44ed-43bd-4d8f-bcd0-9c6e8b5836a2","owner":[],"postedDate":"September 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-02T15:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-06 03:06:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7361619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7361619","identity":"rs-7361619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}