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

Prediabetes is the quiet warning zone between normal metabolism and full-blown type 2 diabetes. Characterised by fasting glucose between 5.6 and 6.9 mmol/L, impaired glucose tolerance, or glycated haemoglobin between 5.7% and 6.4%, it progresses to overt diabetes at a rate of roughly 5% to 10% per year. Yet that trajectory is not fixed: landmark lifestyle interventions have cut diabetes incidence by up to 58%. With an estimated 537 million adults worldwide now living with diabetes and projections reaching 783 million by 2045, finding accessible, sustainable forms of exercise for the prediabetic population has become a global priority.

The trial, conducted at the Health Management Center of Wuxi Hospital of Traditional Chinese Medicine in Jiangsu Province between July and December 2024, enrolled 62 community-dwelling adults aged 45 and older. Participants were randomly assigned in equal measure to one of three groups: a control group receiving standard lifestyle counselling, a Traditional Chinese Exercise group practising Baduanjin, and a Kuanle Equipment Exercise group using purpose-built resistance and aerobic machines. The researchers measured fasting plasma glucose, two-hour oral glucose tolerance, HbA1c and serum 25-hydroxyvitamin D3 at baseline and again at week twelve, using validated laboratory methods with tight quality control.

The Baduanjin protocol was faithfully classical: three 45-minute supervised sessions per week, each comprising a warm-up, thirty minutes covering three cycles of the eight standardised movements, and a cool-down. The equipment group matched the frequency and duration but followed a circuit of leg press, chest press, rowing and stationary cycling at 60% to 70% of heart rate reserve, supervised by ACSM-certified specialists. Adherence was defined as completing at least 80% of the 36 scheduled sessions, and remarkably, all 62 participants finished the study with complete data, a 100% retention rate the authors attribute to monthly phone follow-ups, weekly reminders and flexible scheduling.

The metabolic results were unambiguous. Both exercise groups showed significant improvements over control in every primary glycaemic measure after adjustment for baseline values. Fasting glucose fell from 6.4 to 5.8 mmol/L in the Baduanjin group and from 6.6 to 5.6 mmol/L in the equipment group, while the control group barely moved. Two-hour glucose tolerance improved by 14.6% in the traditional exercise group and 21.2% in the equipment group. HbA1c, the three-month average of blood sugar, dropped from 6.0% to 5.6% with Baduanjin and from 6.2% to 5.4% with machines, whereas controls saw only a trivial decline.

Crucially, however, the direct head-to-head comparison between the two exercise modalities did not reach statistical significance for any primary outcome, with p-values ranging from 0.071 to 0.089. The equipment group posted numerically larger mean changes, which is biologically plausible given its combination of aerobic and resistance training, but the authors are careful to frame this as descriptive and hypothesis-generating rather than proof of superiority. The trial was also underpowered: recruitment constraints limited enrollment to 62 participants instead of the planned 90, leaving the study able to detect only large effects in direct comparisons.

The vitamin D findings may prove the most talked-about result. Every single participant entered the study deficient, with a mean baseline 25(OH)D3 of roughly 21.5 nmol/L, far below the 50 nmol/L sufficiency threshold. After twelve weeks, levels surged by 60.6% in the Baduanjin group and 81.9% in the equipment group, while controls rose only marginally. Because all exercise sessions were conducted indoors, differential sunlight exposure cannot explain the increase. The authors speculate that exercise-induced metabolic adaptations, such as reduced sequestration of vitamin D in adipose tissue as fat mass declined, or altered hepatic hydroxylation, may be responsible, though they emphasise these mechanisms remain speculative without direct assays.

An exploratory correlation analysis added a tantalising but cautious footnote: across the full cohort, within-person increases in 25(OH)D3 were inversely associated with changes in two-hour glucose tolerance after adjustment for age, sex, baseline BMI and treatment group, but not with fasting glucose, HbA1c or triglycerides. The researchers stress that these secondary analyses, based on a small sample, cannot establish vitamin D as a mediator of the glycaemic benefits. Notably, even after substantial gains, mean vitamin D concentrations in both exercise groups remained below sufficiency, suggesting that exercise alone cannot replace supplementation, particularly in winter or for those with limited sun exposure.

Beyond glucose and vitamin D, both interventions improved body composition and blood lipids. Triglycerides fell by 15% with Baduanjin and 20% with equipment exercise, LDL cholesterol dropped by roughly 10% to 14%, and skeletal muscle mass increased in both groups, while BMI and body fat percentage declined significantly compared with control. Safety was excellent: only minor, transient musculoskeletal complaints occurred, resolving within a week, and no hypoglycaemic events were recorded during the entire study period.

The trial carries important caveats. Nearly 89% of participants were female, limiting generalisability to men; dietary vitamin D intake, clothing coverage, sunscreen use and seasonal variation were not systematically measured during a study spanning July to December; the trial was single-centre and not prospectively registered in a public registry; and the duration of prediabetes was not recorded. Still, the practical message stands: for middle-aged and older adults hovering on the edge of diabetes, a culturally familiar, low-impact practice like Baduanjin offers measurable metabolic benefits comparable, within the limits of this study, to standardised gym-based training. Larger, longer, prospectively registered trials that track sunlight and dietary vitamin D will be needed to confirm whether the vitamin D surge is a genuine exercise effect, and whether the numerical edge of machine-based training hardens into proven superiority.

Subject of Research: Effects of traditional Chinese exercise versus equipment-based exercise on glycaemic control and vitamin D levels in adults with prediabetes

Article Title: Comparison of Traditional Chinese Exercise and Equipment‐Based Exercise on Glycaemic Control and Vitamin D Levels in Middle‐Aged and Elderly Patients With Prediabetes: A Randomised Controlled Trial

Article References: Lingzhi, X., Chunhua, Z., Wu, L., Jiejin, D., & Yimin, Y. (2026). Comparison of Traditional Chinese Exercise and Equipment‐Based Exercise on Glycaemic Control and Vitamin D Levels in Middle‐Aged and Elderly Patients With Prediabetes: A Randomised Controlled Trial. Endocrinology, Diabetes & Metabolism, 9(5), Article e70326. https://doi.org/10.1002/edm2.70326

Image Credits: AI Generated

DOI: 10.1002/edm2.70326

Keywords: prediabetes, type 2 diabetes, Baduanjin, Qigong, exercise, vitamin D, HbA1c, glycaemic control, randomised controlled trial, insulin resistance, body composition, traditional Chinese exercise

Type-2 diabetes mellitus is a chronic metabolic disorder characterised by elevated blood glucose resulting from insulin resistance and progressive impairment of insulin secretion. Long-term elevation of glucose damages blood vessels and nerves, driving complications that range from kidney failure and vision loss to foot ulcers, stroke and heart disease. Optimal glycaemic control, meaning the maintenance of blood glucose within recommended targets, is therefore the central objective of diabetes management, because it minimises these complications over time. Alongside medication, adherence to a healthy lifestyle, encompassing diet, physical activity, weight management and avoidance of tobacco, is recognised as a cornerstone of diabetic care, and it is precisely this behavioural dimension that the Nigerian team set out to address.

The study was designed as a randomised controlled trial, the most rigorous experimental format for evaluating the effect of an intervention, because random allocation distributes known and unknown confounding factors evenly between groups. It was conducted among patients with Type-2 diabetes over a period of 22 weeks, running from November 2023 to April 2024. Participants were randomly assigned to either an intervention arm or a control arm using block randomisation in blocks of four, with allocation determined by a table of random numbers at a one-to-one ratio. Sixty-three participants were enrolled in each arm, giving a total of 126 patients in the trial. Block randomisation of this kind helps to keep group sizes balanced throughout the recruitment process, which is particularly useful in a clinical setting where enrolment may proceed unevenly.

Data collection relied on an adapted version of the World Health Organization STEPS questionnaire, a standardised instrument widely used in population health research to capture sociodemographic characteristics, clinical features and behavioural risk factors. The researchers administered the questionnaire both before and after the intervention, allowing each participant to serve as their own baseline comparator as well as enabling comparison between the two trial arms. To summarise lifestyle practices in a single interpretable metric, the team employed a composite Health Behaviour Score, which aggregates individual behaviours into an overall measure of how healthy a participant’s daily practices are. This composite approach is valuable because individual behaviours often move in different directions, and a summary score can capture net change more reliably than any single item alone.

The intervention itself was a programme of structured lifestyle modification counselling delivered using the 5 A’s framework, a widely adopted behavioural counselling model that guides clinicians through five sequential steps: assessing the patient’s current behaviour and readiness, advising clear and personalised behaviour change, agreeing on realistic goals collaboratively, assisting the patient with practical strategies and support, and arranging follow-up to sustain momentum. Crucially, the counselling was reinforced by five follow-up telephone reminders containing standard pre-recorded messages delivered in Hausa, the predominant language of the region, and in English. This dual-language, telephone-based reinforcement is a pragmatic adaptation to the realities of Northern Nigerian primary care, where patients may travel long distances, face competing economic demands and cannot easily attend frequent in-person follow-up sessions.

When the researchers compared the two arms after the intervention, the difference in healthy behaviour was striking. Among participants in the intervention group, 73.8 percent showed a post-intervention increase in healthy behaviour scores, compared with just 26.2 percent in the control group. This difference was highly statistically significant, with a chi-squared statistic of 29.3 and a p-value below 0.001, meaning the probability of observing such a difference by chance alone is extremely small. Importantly, the two arms had comparable baseline healthy behaviour scores before the intervention began, with a chi-squared value of 0.145 and a p-value of 0.703, which confirms that the groups started from essentially the same behavioural starting point and strengthens the argument that the counselling, rather than pre-existing differences, drove the change.

The trial also tracked two key biochemical markers of glycaemic control: fasting blood glucose, which measures circulating glucose after an overnight fast, and glycated haemoglobin, or HbA1c, which reflects average blood glucose over the preceding two to three months and is considered the gold standard for assessing long-term glycaemic control. Using the Wilcoxon signed-rank test, a non-parametric statistical method for comparing paired measurements, the team found statistically significant decreases in both markers within each arm after the study period. In the intervention arm, the decrease in median post-intervention fasting blood glucose was significant with a z-statistic of -4.124 and a p-value below 0.001, and in the control arm the decrease was also significant with a z-statistic of -3.225 and a p-value of 0.001. Similarly, post-intervention HbA1c fell significantly in both groups, with the intervention arm showing a z-statistic of -3.774 and a p-value below 0.001, and the control arm a z-statistic of -3.158 and a p-value of 0.002.

However, when the researchers used analysis of covariance, or ANCOVA, to adjust for baseline differences and isolate the specific effect of the counselling intervention on glycaemic control, the picture changed. The effect of counselling on post-intervention fasting blood glucose was not statistically significant, with an F-statistic of 0.000 and a p-value of 0.994, and the effect on post-intervention HbA1c was likewise not significant, with an F-statistic of 1.084 and a p-value of 0.300. In other words, although blood glucose measures improved over the 22 weeks in both groups, the improvement could not be attributed specifically to the counselling programme. Both arms received routine clinical care, and factors such as concurrent medication adjustments, seasonal dietary patterns, or regression to the mean may have contributed to the glucose declines observed in both groups alike.

The authors conclude that structured lifestyle modification counselling significantly improved healthy behaviour among patients with Type-2 diabetes, but produced no significant effect on glycaemic control within the trial’s timeframe. This dissociation between behaviour and biochemistry is not unusual in lifestyle intervention research. Behavioural change is the first link in a long causal chain, and measurable improvements in HbA1c often require sustained change over longer periods, greater intensity of intervention, or larger sample sizes to detect. A 22-week window may simply have been too short for newly adopted dietary and physical activity habits to exert a detectable, group-level effect on glycated haemoglobin, particularly if the magnitude of behavioural change, while statistically robust, was modest in absolute terms.

Nevertheless, the practical implications of the trial are considerable. The researchers suggest that integrating structured lifestyle counselling with telephone reminders may serve as an innovative way to promote healthy lifestyles in busy, resource-limited primary care settings. The approach requires no expensive equipment, no additional clinic visits and no specialist staff beyond trained counsellors, and the use of pre-recorded messages in the local language makes it scalable and culturally accessible. The trial was registered with the Pan African Clinical Trial Registry under number PACTR202601577207835 on 20 January 2026, and the research was fully funded by the authors themselves, with no external funding received. Ethical approval was obtained from the Health Research Ethics Committee of Aminu Kano Teaching Hospital, and all participants provided voluntary written informed consent. As diabetes prevalence continues to climb across sub-Saharan Africa, evidence that a low-cost, telephone-supported counselling model can meaningfully shift patient behaviour represents a promising foundation, and future trials with longer follow-up will be needed to determine whether these behavioural gains can ultimately be converted into the blood sugar improvements that matter most for preventing diabetic complications.

Subject of Research: Effect of structured lifestyle modification counselling on lifestyle practices and glycaemic control in Type-2 diabetes patients in Northern Nigeria

Article Title: Effect of structured lifestyle modification counselling on lifestyle practices and glycaemic control among Type-2 Diabetes patients in Northern Nigeria (SLIC-T2D trial): a randomised controlled trial

Article References: Gana, A. A., Abdulkadir, Z., Damagun, F. M., Ogunyele, T. T., Olawumi, A. L., Iroezindu, G. U., Suleiman, A. K., Kwaku, A. A., Benjamin, T. A., Chinedu, G. M., & Grema, B. A. (2026). Effect of structured lifestyle modification counselling on lifestyle practices and glycaemic control among Type-2 Diabetes patients in Northern Nigeria (SLIC-T2D trial): a randomised controlled trial. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02592-5

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02592-5

Keywords: Type-2 diabetes, lifestyle modification, counselling, randomised controlled trial, glycaemic control, HbA1c, fasting blood glucose, Nigeria, primary care, telephone reminders, Health Behaviour Score, 5 A's framework