Simple Blood Sugar Index Shows Promise for Spotting Diabetes Risk in the Aral Sea Region

In one of the most environmentally damaged corners of the former Soviet Union, a team of researchers has found that a cheap, widely available laboratory calculation may help identify adults at risk of developing diabetes. The study, conducted in the Aral Sea region of Uzbekistan, examined whether the triglyceride-glucose index, a simple mathematical formula derived from two routine blood tests, could serve as a practical marker of dysglycaemia, the umbrella term for blood sugar levels that are elevated above normal but fall short of, or overlap with, a formal diabetes diagnosis. The findings, published in BMC Endocrine Disorders, offer a glimpse into the metabolic health of a population that has rarely been studied in detail, and they arrive with important caveats about what the index can and cannot do.

The setting itself is remarkable. The Aral Sea, once the fourth-largest lake in the world, has shrunk dramatically since the 1960s following Soviet-era irrigation projects that diverted the rivers feeding it. The exposed seabed has become a source of toxic dust storms, and the surrounding region of Karakalpakstan in northwestern Uzbekistan has been associated with a range of health problems, including historically documented iodine deficiency and thyroid dysfunction. Against this backdrop, the researchers enrolled 98 adults from the districts of Muinak, Kanlykul, and Takhtakupir between 2024 and 2025, measuring anthropometric characteristics, fasting and two-hour postprandial glucose, glycated haemoglobin, thyroid hormones, liver enzymes, and a full fasting lipid profile. It is worth noting that urinary iodine was not directly measured in this study; references to iodine deficiency reflect regional contextual documentation rather than individual measurements.

The participants were predominantly women, who made up 74.5 percent of the cohort, and the metabolic picture was striking. Just over half of the participants, 50 out of 98, had dysglycaemia, while 27.6 percent had hypothyroidism, an underactive thyroid gland. This combination of metabolic and endocrine abnormalities in a single, environmentally vulnerable population is precisely what made the study worthwhile, because biochemical data linking thyroid status, elevated liver enzymes, and disturbed glucose metabolism in this region had been scarce until now. The study was approved by the Ethical Committee of the Republican Centre of Endocrinology in Uzbekistan and conducted in accordance with the Declaration of Helsinki, with written informed consent obtained from all participants.

The centrepiece of the analysis was the triglyceride-glucose index, often abbreviated as TyG. This index is calculated from fasting triglyceride and fasting glucose values using a logarithmic transformation, and it has gained attention worldwide as an inexpensive surrogate for insulin resistance, the underlying metabolic disturbance that drives type 2 diabetes. Unlike direct measures of insulin sensitivity, which require specialised assays and are costly, the TyG index relies on two tests that virtually any clinical laboratory can perform. Because fasting triglycerides were available for only 65.3 percent of the participants, all TyG-based analyses were performed on a complete-case subset of 64 individuals, a limitation the authors were transparent about.

Within that subset, the results were compelling. Each unit increase in the TyG index was associated with significantly higher odds of dysglycaemia, with an odds ratio of 2.33 and a 95 percent confidence interval of 1.31 to 4.15, and a p-value of 0.004. Crucially, this association held up after statistical adjustment for body mass index, sex, thyroid-stimulating hormone, and alanine aminotransferase, a liver enzyme. That means the link between TyG and abnormal blood sugar was not simply explained by obesity, thyroid dysfunction, or liver enzyme elevation, all of which could plausibly confound the relationship. The researchers also employed Monte Carlo cross-validation, a resampling technique that repeatedly splits the data to test whether the findings are stable, adding a layer of statistical rigour uncommon in studies of this size.

To assess how well the TyG index could actually discriminate between people with and without dysglycaemia, the team performed receiver operating characteristic analysis, a standard tool for evaluating diagnostic tests. The index achieved an area under the curve of 0.77, with a confidence interval of 0.64 to 0.87, indicating moderate discriminatory power. Using the Youden method, a technique that maximises the combined sensitivity and specificity of a test, the researchers identified a threshold value of 8.70. At this cut-off, the index showed a sensitivity of 43.2 percent, meaning it missed more than half of the people with dysglycaemia, but a specificity of 96.2 percent, a positive predictive value of 94.1 percent, and a positive likelihood ratio of 10.9. In practical terms, a TyG value above 8.70 makes dysglycaemia very likely, but a value below it by no means rules the condition out.

The authors were unusually direct about this limitation. They stated that the sensitivity of 43.2 percent at the 8.70 threshold means this cut-off cannot replace comprehensive screening and should be used only for high-confidence rule-in of dysglycaemia. This is an important message in an era when simple biomarker scores are often oversold as replacements for proper clinical testing. A test with high specificity but modest sensitivity is best understood as a confirmatory signal rather than a first-line screening tool: it can flag people who almost certainly need further evaluation, but it cannot reassure those below the threshold that their metabolism is normal. For a condition like dysglycaemia, where early detection genuinely changes outcomes, that distinction matters enormously.

The study also compared the TyG index against an alternative surrogate measure of insulin resistance called METS-IR, the metabolic score for insulin resistance, which incorporates body mass index and glucose alongside lipid measures. METS-IR yielded a comparable area under the curve of 0.79, with a confidence interval of 0.67 to 0.89. Because the confidence intervals of the two indices overlapped substantially, the researchers concluded that there was no statistically significant superiority of one over the other. This kind of head-to-head comparison is valuable, because the literature on insulin resistance surrogates is crowded with competing scores, and studies rarely test whether a more complex formula actually outperforms a simpler one in a given population.

Another notable finding concerned the thyroid. Given the region’s history of iodine deficiency and the high prevalence of hypothyroidism in the cohort, one might have expected thyroid dysfunction to be linked with disturbed glucose metabolism. It was not: after adjustment, hypothyroidism showed no independent association with dysglycaemia, with an adjusted odds ratio of 0.75. This suggests that in this population, the metabolic disturbance captured by the TyG index operates largely independently of thyroid status, though the modest sample size means the possibility of a real but undetected relationship cannot be fully excluded. The researchers also compared participants with and without available triglyceride data and found that the two groups differed significantly only in body mass index, at 32.7 versus 29.3 kilograms per square metre, suggesting the complete-case subset was somewhat more obese than the cohort as a whole.

The authors framed their conclusions carefully, describing the findings as hypothesis-generating and emphasising that external validation in a larger cohort is required before any recommendation for clinical implementation can be made. That caution is warranted. The study was cross-sectional, meaning it captured a single moment in time and cannot establish whether a high TyG index precedes the development of dysglycaemia or merely accompanies it. The sample was small and heavily female, and triglyceride data were missing for about a third of participants. Still, the work represents a meaningful contribution: it brings modern metabolic epidemiology to a region whose health has been shaped by one of the twentieth century’s worst environmental disasters, and it suggests that a two-dollar laboratory calculation could, with proper validation, become part of the toolkit for identifying people at risk of diabetes in resource-limited settings around the world.

Subject of Research: Association between the triglyceride-glucose index and dysglycaemia in adults from the Aral Sea region of Uzbekistan

Article Title: Triglyceride-glucose index and dysglycaemia in adults from the Aral Sea region of Uzbekistan: a cross-sectional study

Article References: Ainazarova, Z., Kamalov, T., Alieva, A. V., & Shamansurova, Z. (2026). Triglyceride-glucose index and dysglycaemia in adults from the Aral Sea region of Uzbekistan: a cross-sectional study. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02539-w

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02539-w

Keywords: triglyceride-glucose index, dysglycaemia, insulin resistance, type 2 diabetes, hypothyroidism, Aral Sea, Karakalpakstan, Uzbekistan, cross-sectional study, ROC analysis, METS-IR, metabolic disorders