Longitudinal Metabolic Trajectories in Diabetes Prevention Program Participants Reveal Subgroups With Varying Micro- and Macrovascular Complication Risks

Abstract

Objective: Type 2 diabetes (T2D) and its associated complications develop heterogeneously over decades, but few studies span the progression from prediabetes to clinical events. We investigated whether long-term metabolic trajectories beginning in prediabetes delineate subgroups with differential complication risk.

Research design and methods: Clinical data from 1,732 Diabetes Prevention Program/Outcomes Study participants (follow-up 19 years) were analyzed across 12 phenotypes. Tensor decomposition was used to capture longitudinal patterns, and Gaussian mixture modeling was used to define longitudinal clusters. Cluster-specific complications were quantified with Cox and logistic regression.

Results: Four clusters emerged. Clusters 1 and 2 (73% of participants) maintained stable glycemia, blood pressure, and lipids. Although 49% and 71%, respectively, developed T2D, cumulative micro- and macrovascular events remained low. Cluster 3 (12%) showed the steepest rise in insulin resistance and hyperglycemia, with 92% of the subgroup progressing to T2D and a markedly higher rate of retinopathy (odds ratio [OR] 8.8, 95% CI 3.9-20.1) and neuropathy (OR 3.4, 95% CI 2.1-5.5). Cluster 4 (15%) presented with baseline microalbuminuria often prior to the development of T2D (73%). It was distinguished by progressive estimated glomerular filtration rate decline and a doubling of cardiovascular events (hazard ratio 2.0, 95% CI 1.4-3.0), despite serum lipids comparable with other groups.

Conclusions: Two-thirds of individuals with prediabetes follow metabolically resilient trajectories, whereas distinct insulin-resistant or renal-dysfunction trajectories precede micro- or macrovascular complications, respectively. The optimal window for macrovascular complication prevention in individuals with prediabetes microalbuminuria may precede progression to T2D.

Graphical abstract

Figure 1

Dimensionality reduction and clustering of longitudinal metabolic trajectories of DPP/DPPOS participants over 19 years of follow-up. A: The DPP/DPPOS data were interpolated using Gaussian smoothing and restructured into a three-dimensional tensor with 1,732 participants × 19 years (38 time points) × 12 phenotypes, resulting in 789,792 data points to be analyzed. DBP, diastolic blood pressure; glucose, fasting glucose; insulin, fasting insulin; SBP, systolic blood pressure; TC, total cholesterol; waist C, waist circumference. B: Tensor decomposition using PARAFAC was performed to reduce the dimensionality of the data. The reduced dimensional representation consists of a series of loading vectors corresponding to each of the original dimensions: individuals, clinical phenotypes, and time. Twelve sets of loading vectors (i.e., rank) optimally reconstruct the original data. A matrix composed of the individuals’ loading vectors over all 12 ranks was extracted for downstream clustering. C: Participant loading vectors were clustered using GMM. UMAP, Uniform Manifold Approximation and Projection. D: Participant clusters from C were used to compute cluster-specific phenotype trajectories from A. E: Participant clusters from C were used to analyze diabetes incidence and the occurrence of clinical complications. Validation outcomes include diabetes diagnosis and renal dysfunction. Withheld outcomes include eMACE, retinopathy, and neuropathy.

Figure 2

Clustering of longitudinal metabolic trajectories and temporal trends by cluster. A: Individual loading vectors produced by the tensor decomposition were visualized in two dimensions using the Uniform Manifold Approximation and Projection (UMAP) algorithm with hyperparameters set to a minimum distance = 0.5 and neighbors = 10. Each point representing 1 of the 1,732 individuals was colored by their associated cluster assignment (n = 544, n = 714, n = 215, and n = 259). B: Visualization of the cluster probabilities per individual grouped by assigned cluster. Each subplot represents a cluster, with title indicating cluster label. Each stacked vertical bar on the subplot represents an individual’s probabilities for belonging to each of the four clusters. Individuals are assigned to the subplot (i.e., cluster) for which they have the highest probability. Most individuals have a high probability for a single cluster, but some individuals (indicated by multicolored bars) could belong to multiple clusters. C: Each subplot shows clinical phenotype trajectories by visit, where each visit indicates a 6-month interval. The average temporal trajectories computed from the smoothed and interpolated data for each phenotype are shown colored by cluster. Some phenotypes show stable trends over time (e.g., BMI, waist circumference), whereas others vary temporally in specific clusters (e.g., increasing fasting insulin in cluster 3). Some phenotypes vary at baseline and differences persist over time (e.g., uACR), whereas other phenotypes are similar at baseline but exhibit differing trajectories over time (e.g., fasting glucose, A1C). DBP, diastolic blood pressure; glucose, fasting glucose; insulin, fasting insulin; SBP, systolic blood pressure; TC, total cholesterol; waist C, waist circumference.

Figure 3

Progression to T2D and complications by cluster. A–C: Kaplan-Meier curves for incidence of diabetes and complications. Each cluster is represented by a different colored line. Each visit indicates a 6-month interval. Values below indicate counts (dropout). A: Probability of diabetes diagnosis. B: Probability of renal dysfunction defined as two occurrences of eGFR <60 mL/min/1.73 m² within 1.5 years. C: Probability of eMACE. Cluster 1 vs. cluster 4 show significant differences in the eMACE hazard ratio based on Cox regression. D: Bar chart of the proportion of retinopathy by cluster. Significant differences in the odds ratio were observed in cluster 1 vs. 3 and cluster 1 vs. 4 by logistic regression. E: Bar chart of the proportion of neuropathy by cluster. Significant differences in the odds ratio were observed in cluster 1 vs. 3 by logistic regression. ***P < 10⁻⁶; **P < 0.001; *P < 0.017; ns, not significant. All statistical tests included the following covariate adjustments: age, sex, treatment, smoking status, lipid-lowering medication, antihypertensive medication, and glucose-lowering medication.