Which individual randomized controlled trials showed the largest effects on A1c, and what were their designs and risk‑of‑bias assessments?
Executive summary
Three types of randomized controlled trials stand out in contemporary reporting for producing the largest individual reductions in hemoglobin A1c (absolute percent change): intensive multi‑year glycemic‑control trials (classic trials like DCCT/UKPDS), a small set of behavioral/dietary randomized trials that produced large early A1c falls (e.g., very‑low‑calorie or low‑carbohydrate diet arms), and recent digital‑health/app trials that report sizable subgroup effects; however, design features and bias risks vary widely across these studies, limiting simple comparisons [1] [2] [3] [4].
1. Which individual randomized trials reported the biggest A1c drops
Landmark long‑duration trials produced large between‑group A1c separations: DCCT and UKPDS randomized patients to intensive versus conventional glycemic strategies and reported differences on the order of ~0.9 percentage points or greater in mean A1c over years, with durable effects on microvascular outcomes (DCCT/UKPDS summaries) [1] [2]. In more contemporary, shorter trials, some dietary or behavioral interventions have reported large short‑term within‑group or between‑group reductions: for example, randomized comparisons of very‑low‑calorie diets initially showed larger early A1c declines relative to comparators though differences often attenuated over time (Wing et al. randomized VLCD vs low‑calorie diet) [3]. Among digital‑health RCTs, a single‑center app trial reported a sizable subgroup A1c decline of -0.81% at 12 weeks in participants with baseline A1c ≥7.5% in the intervention arm versus control [4].
2. What those trials actually looked like—designs and populations
The DCCT and UKPDS were large, multi‑year randomized trials comparing intensive multifactorial glycemic regimens (often multiple drugs/insulin and tight targets) against standard care, with long follow‑up to capture complications; DCCT reported mean achieved A1c differences of roughly 1 percentage point and UKPDS similar magnitudes [1] [2]. The Wing VLCD dietary RCT randomized 93 obese people with T2D to VLCD versus low‑calorie formula approaches for one year with weight and A1c endpoints, showing larger initial weight loss and early A1c reduction that did not necessarily persist to one year [3]. The BMC Medicine app‑based trial was a 12‑week single‑center, randomized 1:2 study of a smartphone personal health record plus individualized motivational texts versus app alone in overweight/obese people with T2D, reporting a subgroup A1c reduction of -0.81% for those with baseline A1c ≥7.5% [4]. The DAVOS multicenter digital trial was an open‑label, 6‑month randomized study across 21 centers of an app (ESYSTA) versus standard care in insulin‑treated patients, designed as pragmatic and unblinded [5].
3. Risk‑of‑bias assessments: common weaknesses and strengths
Across systematic reviews and meta‑analyses, risk‑of‑bias patterns recur: inability to blind participants and personnel in behavioral, lifestyle, and app trials raises concerns in domains related to performance and detection bias; many meta‑analyses used RoB 2.0 or JBI tools and flagged per‑protocol analyses, missing outcome data, and inadequate reporting as drivers of “some concerns” or high risk [6] [7] [8]. The DAVOS trial explicitly acknowledged the open‑label design and lack of placebo as an important limitation that could bias patient‑reported outcomes [5]. Large long‑duration trials such as DCCT/UKPDS had rigorous randomization and follow‑up protocols that strengthened internal validity, though their intensive pharmacologic strategies included many concomitant interventions that complicate attribution to any single component [1] [2].
4. Interpreting the magnitude: heterogeneity, subgroups, and durability
Reported “largest” A1c effects are heterogeneous in meaning: a subgroup‑specific -0.81% over 12 weeks in a digital‑health RCT (app trial) is clinically promising but derived from a short trial and a subset analysis, which increases the chance of overestimating true effect and raises concerns about multiplicity and external validity [4]. VLCD and other diet trials can show brisk early declines that attenuate by one year [3]. By contrast, the durable ~0.9–1.0% A1c separations in DCCT/UKPDS came from trials explicitly powered and designed to test long‑term outcomes [1] [2]. Systematic reviews note substantial heterogeneity across exercise and lifestyle trials and classify many individual trials as having “some concerns” or higher RoB because of deviations from intended interventions or lack of blinding [9] [6].
5. Bottom line: which RCTs truly “showed the largest” effects and how confident to be
The largest and most robust absolute A1c separations historically come from the classic intensive glycemic‑control trials (DCCT/UKPDS) with durable clinical outcome data and lower risk of randomization bias [1] [2]. Shorter modern trials—VLCD dietary RCTs and some digital‑health interventions—report large early or subgroup A1c drops (e.g., -0.81% subgroup in the app RCT and early VLCD effects) but are limited by open‑label designs, subgroup reporting, and attenuation over time, issues repeatedly flagged by RoB tools and meta‑analyses [4] [3] [6]. Where certainty matters for practice, prioritize large, well‑conducted RCTs with prespecified endpoints and low RoB; where innovation matters, consider promising short‑term trials as signals requiring larger, blinded, and longer trials to confirm durability and generalizability [6] [5] [7].