Diabetes in South Korea Shifted Sharply in 2022, National Survey Analysis Reveals

Diabetes care in South Korea appears to have crossed a threshold. A new analysis of a decade of national health data suggests that the makeup of the country’s diabetes population changed abruptly in 2022, with a sharp rise in the share of people whose blood sugar is well controlled on oral medication, and a corresponding decline in those who are either untreated or running dangerously high glucose levels. The study, published in BMC Endocrine Disorders, draws on the Korea National Health and Nutrition Examination Survey, or KNHANES, a nationally representative surveillance program that has been tracking the health and diet of Korean citizens for decades. Rather than treating diabetes as a simple binary of controlled versus uncontrolled, the researchers carved the population into four distinct profiles that combine two pieces of information: how high a person’s glycated haemoglobin, or HbA1c, has climbed, and how far their treatment has progressed along the therapeutic ladder.

HbA1c is a familiar number to anyone living with diabetes. It reflects the average blood glucose level over the preceding two to three months, because glucose binds irreversibly to haemoglobin in red blood cells in proportion to its concentration in the bloodstream. A value below 5.7 percent is considered normal, while 6.5 percent or above meets the diagnostic threshold for diabetes. The research team, led by Soo-Hyun Sung of Wonkwang University and corresponding authors Kwang-Il Park of Gyeongsang National University and Young Woo Kim of Dongguk University, used this marker together with treatment status to define four phenotypes. P1 describes people on no treatment or lifestyle modification alone whose HbA1c remains below 7 percent, the conventional target for glycaemic control. P2 captures those who have reached their target while taking oral glucose-lowering medication. P3 covers people with HbA1c between 7 and 9 percent who are not yet on insulin, and P4 is the most severe category, comprising anyone using insulin or whose HbA1c has risen to 9 percent or higher.

The dataset behind the analysis is substantial. From the survey years 2015 through 2024, the researchers identified 8,702 adults aged 30 or older who met at least one of four criteria for diabetes: a physician diagnosis, current use of glucose-lowering medication, a fasting glucose of 126 milligrams per decilitre or more, or an HbA1c of 6.5 percent or above, combined with a measured HbA1c value. Because KNHANES uses a complex, stratified, multistage sampling design, the team applied survey weights and design-based statistical methods throughout, ensuring that their estimates reflect the Korean adult population rather than the quirks of the sample. The weighted distribution across the four phenotypes came out as 24.8 percent in P1, 34.3 percent in P2, 28.9 percent in P3, and 12.0 percent in P4.

The headline finding is not the distribution itself but its sudden rearrangement. A formal change-point analysis, which tests whether a time series is better explained by a single trend or by two segments separated by an abrupt shift, selected 2022 as the break point for both the P2 and P3 phenotypes. The selection was not marginal. The year 2022 was chosen by both the design-based Akaike information criterion, a statistic that penalises model complexity, and the maximum Wald statistic, and it reappeared in 97.1 percent and 97.7 percent of 1,000 design-based bootstrap replicates for P2 and P3 respectively. Critically, a discrete level shift fitted the data better than a gradual change in slope, suggesting a step change rather than a slow drift in the composition of the diabetes population.

The magnitude of the shift is striking. The share of people in the treated, at-target phenotype P2 rose from 28.2 percent during 2015 to 2021 to 46.8 percent during 2022 to 2024, a relative increase of 66 percent, equivalent to 18.6 percentage points. After adjustment for covariates, the prevalence ratio for belonging to P2 in the later period was 1.49, with a 95 percent confidence interval of 1.31 to 1.69. Over the same interval, P1 and P3 both declined, with adjusted prevalence ratios of 0.81 and 0.68 respectively, both statistically significant, while P4, the insulin-treated or severely hyperglycaemic group, remained essentially unchanged. In practical terms, the population appears to have migrated from the untreated and above-target categories into the well-controlled, orally treated category, while the most severe group held steady.

Here the study delivers its most important caveat. The year 2022 is also the first year of KNHANES cycle IX, a redesign of the survey program. The authors are explicit that the observed discontinuity cannot be attributed to genuine changes in diabetes care within this study design, because a change in survey methodology, sampling frame, or measurement procedures would produce exactly the kind of step change they detected. This is a recurring challenge in repeated cross-sectional surveillance: when the instrument changes, the signal and the artefact become difficult to disentangle. The researchers therefore frame their temporal finding as a description of a redistribution in the data, one whose clinical interpretation requires confirmation from other data sources and survey designs.

The analysis of correlates, the factors associated with belonging to one phenotype rather than another, also carries a methodological twist. Because the reference category P1 is dominated by screen-detected cases, 88 percent of its members had never been previously diagnosed, comparisons against it can generate associations that reflect diagnostic status rather than biology. When the researchers restricted the analysis to previously diagnosed participants, most of the associations seen in the full sample shrank or vanished. Reduced kidney function, defined as an estimated glomerular filtration rate below 60, was the only association with the insulin-treated or markedly hyperglycaemic phenotype that retained its full magnitude, with an odds ratio of 2.67 and a confidence interval of 1.27 to 5.62. This makes physiological sense: declining renal function is both a complication of long-standing diabetes and a factor that shapes treatment decisions, often pushing clinicians toward insulin-based regimens.

Reported dyslipidaemia, the presence of abnormal blood lipid levels, persisted as a correlate of the P2 and P3 phenotypes but at roughly half its full-sample strength, with an odds ratio of 1.79 in the diagnosed-only analysis. Meanwhile, the inverse associations with obesity and alcohol consumption that appeared in the full sample disappeared entirely once the comparison was restricted to diagnosed individuals, a pattern the authors attribute to the composition of the screen-detected reference group. In the full sample, reported descriptively because of this reference-group problem, the corresponding estimates for reduced kidney function were considerably larger, at 3.09 for both comparisons. The lesson is a general one for observational diabetes research: who ends up in your comparison group can manufacture or erase associations, and screening status is a powerful hidden variable.

The robustness work behind the paper is unusually thorough for a survey analysis. A sensitivity analysis using multiple imputation, a statistical technique that fills in missing covariate values by drawing from their predicted distributions, reproduced every complete-case estimate closely, with the largest discrepancy amounting to just 0.17 on the odds-ratio scale. The temporal findings survived three alternative classification rules, and the estimated change-point remained 2022 under each of them. Of 42 interaction tests examining whether the associations differed across subgroups, none remained significant after correction for the false discovery rate, the expected proportion of false positives among declared findings. These checks do not eliminate the survey-redesign confound, but they do indicate that the reported patterns are not artefacts of missing data or arbitrary analytical choices.

The authors are careful about what their classification does and does not claim. The four phenotypes describe where people currently sit on the combined axes of glycaemic control and treatment intensity; they are not a prognostically validated staging system, and the scheme’s ability to predict complications, cardiovascular events, or mortality remains to be tested in longitudinal and independent populations. Still, the study offers a template for how national surveillance data can be mined more intelligently. By replacing the blunt controlled-versus-uncontrolled dichotomy with a composite phenotype that respects the heterogeneity of the diabetes population, and by subjecting temporal trends to formal change-point analysis with bootstrap validation, the researchers have shown both the promise and the peril of such approaches. The 2022 discontinuity in Korean diabetes data may eventually prove to reflect real changes in care, new drug classes such as SGLT2 inhibitors and GLP-1 receptor agonists reshaping treatment patterns, or simply the fingerprint of a redesigned survey. Until that question is resolved with independent evidence, the study stands as a rigorous description of a population in flux, and a reminder that in health statistics, the measuring instrument is itself part of the measurement.

Subject of Research: Glycaemic–treatment phenotypes of diabetes and their temporal change among Korean adults

Article Title: Glycaemic–treatment phenotypes of diabetes and their correlates among Korean adults: a repeated cross-sectional analysis of the Korea National Health and Nutrition Examination Survey, 2015–2024

Article References: Sung, S.-H., Lee, J., Park, H.-G., Lee, J.-H., Sung, H.-K., Lee, S.-U., Yang, J.-H., Park, K.-I., & Kim, Y. W. (2026). Glycaemic–treatment phenotypes of diabetes and their correlates among Korean adults: a repeated cross-sectional analysis of the Korea National Health and Nutrition Examination Survey, 2015–2024. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02573-8

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02573-8

Keywords: diabetes, HbA1c, KNHANES, phenotype classification, change-point analysis, glycaemic control, insulin treatment, chronic kidney disease, dyslipidaemia, South Korea, survey methodology, epidemiology