The study, led by Jiang Mei and colleagues at Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, drew on a longitudinal health examination cohort in China, following 15,442 adults who entered the study with impaired fasting glucose. Over approximately three years of follow-up, 7,230 participants, or 46.8 percent, reverted to normoglycemia. That strikingly high reversion rate alone is worth pausing on, because it underscores how dynamic early dysglycemia really is. The researchers then asked a deceptively simple question: did the creatinine-to-body weight ratio, abbreviated Cre/BW, predict who would cross back over the line?

Creatinine is a waste product generated by muscle metabolism, and because it is cleared by the kidneys at a fairly steady rate, it has long served as a window into both kidney function and, indirectly, muscle mass. Dividing serum creatinine by body weight produces a crude but intriguing index: a person with substantial muscle relative to their body size will tend to have a higher ratio than someone carrying more fat mass for the same weight. In an era when sarcopenic obesity, the combination of reduced muscle and excess fat, is increasingly recognized as a metabolic hazard, such a ratio captures something that body mass index alone cannot.

The statistical machinery behind the study was correspondingly careful. The researchers used Cox proportional hazards models, the standard tool for analyzing time-to-event data, to estimate how the rate of reversion changed with each unit increase in the Cre/BW ratio, multiplied by 100 for numerical convenience. Missing covariate data were handled with multiple imputation across 50 imputed datasets, a robust approach that preserves statistical power while honestly accounting for uncertainty introduced by incomplete records. The fully adjusted model controlled for a battery of potential confounders, meaning the association survived even after accounting for factors that might otherwise explain it away.

The headline result was unambiguous. Each one-unit increase in the Cre/BW ratio was associated with a 34 percent higher rate of reversion to normoglycemia, with a hazard ratio of 1.34 and a 95 percent confidence interval of 1.23 to 1.47. When participants were sorted into quartiles of the ratio, the association rose in a graded fashion across the groups, with a P value for trend below 0.001. In plain terms, the more muscle mass a person appeared to carry relative to their body weight, the more likely their fasting glucose was to return to the normal range within the follow-up window.

But the relationship was not a straight line. Using restricted cubic splines, a flexible modeling technique that allows the shape of an association to bend rather than forcing it into a linear straitjacket, the researchers found statistically significant nonlinearity, with a P value of 0.043. The data revealed an inflection point at a Cre/BW ratio of 1.481, meaning the strength of the association changed character around that threshold. This kind of nonlinear structure matters clinically. If the benefit of a higher ratio plateaus or accelerates at particular values, then a single average effect estimate can mislead, and risk stratification tools built on linear assumptions may misclassify patients near the turning point.

Perhaps the most conceptually interesting part of the study is what happened when the researchers probed why the association exists. They turned to mediation analysis, an exploratory statistical framework that asks whether a third variable lies on the causal pathway between an exposure and an outcome. The mediators they chose were three lipid-related metabolic indices that have become staples of cardiometabolic research: the triglyceride-to-high-density lipoprotein cholesterol ratio, the atherogenic index of plasma, and the triglyceride-glucose index, known as TyG. Each of these captures a different facet of the dyslipidemia and insulin resistance that accompany prediabetes.

The mediation estimates were substantial. The TG/HDL-C ratio statistically accounted for an estimated 28.5 percent of the association between Cre/BW and reversion, while the atherogenic index of plasma mediated an estimated 49.2 percent and the TyG index 49.9 percent. In other words, roughly half of the observed link between the muscle-related ratio and the return of normal blood sugar could be explained statistically by improvements in these lipid-glucose indices. The authors are appropriately cautious here, noting that the mediation findings should be considered exploratory. Mediation analysis in observational data can suggest plausible mechanisms, but it cannot prove causation, and the indices themselves are mathematical composites that may overlap with the outcome measure in complicated ways.

Even with those caveats, the biological story hangs together coherently. Skeletal muscle is the body’s dominant site of glucose disposal, soaking up the majority of circulating glucose after a meal and during fasting through insulin-dependent and insulin-independent pathways. Greater muscle mass relative to body weight implies a larger metabolic sink for glucose and typically better insulin sensitivity. Fat mass, by contrast, particularly visceral fat, promotes insulin resistance and drives the elevated triglycerides and depressed HDL cholesterol that the TG/HDL-C ratio and the atherogenic index of plasma capture. A higher Cre/BW ratio may therefore flag a body composition that favors glucose clearance, and the lipid indices may be the intermediate signature of that favorable physiology.

The findings were consistent across sensitivity analyses, which strengthens confidence that the results are not an artifact of a particular modeling choice. The study is a secondary analysis of anonymized data obtained from the Dryad Digital Repository, so it inherits the limitations of its source cohort, and observational associations of this kind cannot establish that raising one’s creatinine-to-body weight ratio would itself cause reversion to normoglycemia. Still, the work adds to a growing body of evidence that body composition, not just body size, shapes the trajectory of early dysglycemia. For clinicians, the message is that a routine blood test already on nearly every metabolic panel, read alongside body weight, may carry prognostic information about which patients with impaired fasting glucose are positioned to recover. For patients, it reinforces the familiar but newly quantified advice that preserving and building muscle, alongside managing blood lipids, may be among the most powerful levers for pulling blood sugar back from the brink.

Subject of Research: Association of the creatinine-to-body weight ratio with reversion from impaired fasting glucose to normoglycemia and mediation by lipid metabolic indices

Article Title: Creatinine-to-body weight ratio and reversion from impaired fasting glucose to normoglycemia: nonlinear associations and mediation by lipid indices (TyG, TG/HDL-C, AIP)

Article References: Mei, J., Xiao, X., Li, G., Liang, X., Lai, K., Zou, Z., Zhou, Y., Li, Y., & Gui, S. (2026). Creatinine-to-body weight ratio and reversion from impaired fasting glucose to normoglycemia: nonlinear associations and mediation by lipid indices (TyG, TG/HDL-C, AIP). BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02544-z

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02544-z

Keywords: impaired fasting glucose, normoglycemia, creatinine-to-body weight ratio, prediabetes, type 2 diabetes, triglyceride-glucose index, TG/HDL-C ratio, atherogenic index of plasma, insulin resistance, muscle mass, mediation analysis, cohort study

The research, led by Özden Gökdemir of the Department of Family Medicine at İzmir University of Economics together with Olgu Aygün and Ayça Asma Sakallı, took a retrospective look at the medical records of adults who attended the Göztepe District Family Medicine Outpatient Clinic of Bozyaka Training and Research Hospital in Turkey between August 2021 and August 2023. From that window, the team assembled a cohort of 1,532 individuals aged 18 and over, all of whom had the laboratory measurements needed to calculate insulin resistance directly. Crucially, rather than relying on values recorded in the charts, the investigators recalculated the homeostatic model assessment of insulin resistance, or HOMA-IR, for every participant from the raw fasting glucose and fasting insulin measurements using the standard formula: fasting glucose in milligrams per deciliter multiplied by fasting insulin in microunits per milliliter, divided by 405.

HOMA-IR is a mathematical shorthand for how hard the pancreas must work to maintain normal blood sugar. Higher values indicate that more insulin is required to achieve the same glucose control, which is the essence of insulin resistance. The researchers drew a line at 2.5, a commonly used threshold: participants above it formed the case group of 428 people, while the 1,104 participants at or below it served as controls. This stratification allowed the team to ask a deceptively simple question—which routinely measured blood parameters separate insulin-resistant individuals from their insulin-sensitive peers?

The answer, at first glance, was a familiar cast of metabolic suspects. Compared with controls, the insulin-resistant group was significantly older and showed higher neutrophil counts, fasting glucose, glycated hemoglobin (HbA1c), triglycerides, insulin, HOMA-IR, and triglyceride-to-HDL cholesterol ratios, alongside lower levels of high-density lipoprotein cholesterol, the so-called good cholesterol. Total cholesterol and low-density lipoprotein cholesterol, the targets of most conventional cholesterol screening, did not differ significantly between the groups. That last detail carries a subtle but important message: the standard lipid panel’s headline numbers may look reassuring in someone whose metabolism is quietly unraveling, while the relationship between triglycerides and HDL cholesterol tells a more revealing story.

Sex mattered as well. Women had significantly higher HDL cholesterol levels than men in both groups, a difference that held with a p-value below 0.001. This biological gap is well recognized—estrogen tends to raise HDL cholesterol—and it underscores why sex-adjusted interpretation of lipid markers matters when clinicians are trying to gauge metabolic risk. The study’s statistical machinery accounted for this by adjusting its final models for both age and sex, ensuring that the associations reported were not merely artifacts of demographic differences between the groups.

Correlation analysis revealed one of the strongest relationships in the dataset: triglycerides and the TG/HDL-C ratio moved together almost in lockstep, with a correlation coefficient of roughly 0.9 in both the case and control groups and a p-value below 0.001. This near-identity is not surprising to lipid biochemists, since the ratio is literally constructed from triglyceride levels divided by HDL cholesterol, but it raises a practical question—if the two measures carry nearly the same information, does the ratio add anything beyond triglycerides alone? The answer lies in how each performs as a screening tool and how they behave in multivariable models, where the ratio ultimately outlasted its components.

To quantify how well each candidate marker could discriminate between insulin-resistant and insulin-sensitive individuals, the team turned to receiver operating characteristic, or ROC, curve analysis. This technique plots true-positive rates against false-positive rates across all possible cut-off values, and the area under the resulting curve, or AUC, expresses discriminative ability on a scale from 0.5, equivalent to a coin flip, to 1.0, perfect classification. Triglycerides achieved an AUC of 0.641, with a 95 percent confidence interval of 0.610 to 0.672, and an optimal cut-off of at least 106 milligrams per deciliter. The TG/HDL-C ratio performed comparably, with an AUC of 0.644 (95 percent confidence interval 0.612 to 0.675) and a cut-off of at least 1.96. These are modest numbers—useful signal, but far from diagnostic certainty.

The neutrophil-to-lymphocyte ratio, or NLR, told a very different story. This marker, calculated simply by dividing the absolute neutrophil count by the absolute lymphocyte count from a standard complete blood count, has attracted enormous attention in recent years as a cheap, widely available proxy for systemic inflammation. Because low-grade inflammation is implicated in the pathogenesis of insulin resistance and type 2 diabetes, many researchers have hoped the NLR could serve as an early-warning flag. In this cohort, the hope did not materialize. The NLR showed an AUC of just 0.517—statistically indistinguishable from random guessing—and in the multivariable logistic regression adjusted for age and sex, it carried an odds ratio of 0.99 with a p-value of 0.712, meaning it contributed essentially nothing to predicting insulin resistance once other factors were considered.

The TG/HDL-C ratio, by contrast, remained independently associated with insulin resistance even after adjustment, with an odds ratio of 1.20 per unit increase (95 percent confidence interval 1.13 to 1.27; p < 0.001). In plain terms, every one-unit rise in the ratio was associated with a 20 percent increase in the odds of being insulin resistant. This durability across statistical models matters, because it suggests the association is not simply explained by the insulin-resistant group being older or by sex differences in lipid physiology. The result aligns with a growing international literature linking the TG/HDL-C ratio—and related indices such as the triglyceride-glucose index and its body mass index and waist circumference variants—to metabolic dysfunction across diverse populations.

Why would this particular ratio track insulin resistance so faithfully? The mechanistic story begins in the liver. When insulin’s ability to suppress fat breakdown in adipose tissue falters, more free fatty acids flood the liver, driving hepatic triglyceride production and the release of triglyceride-rich very-low-density lipoprotein particles into the bloodstream. At the same time, insulin resistance is entangled with lower HDL cholesterol, partly through the activity of cholesteryl ester transfer protein, which shuttles lipids between lipoprotein classes and tends to deplete HDL of its cholesterol cargo when triglycerides are abundant. The result is a lipid signature—high triglycerides, low HDL—that is both a consequence and a companion of insulin resistance, and the TG/HDL-C ratio captures it in a single, easily computed figure.

The authors are careful about what their findings do and do not support. With AUC values hovering in the mid-0.6s, neither triglycerides nor the TG/HDL-C ratio can stand alone as a diagnostic test for insulin resistance; their discriminative ability is too modest for that burden. Instead, the study positions them as adjunctive screening indices—simple, practical, and accessible numbers that a family physician can glance at during a routine visit and use to decide whether deeper investigation, such as fasting insulin measurement and formal HOMA-IR calculation, is warranted. In primary care settings where specialized metabolic testing is scarce or costly, that kind of triage tool has genuine value.

The study’s design also imposes limits worth keeping in view. As a retrospective, cross-sectional analysis of a single outpatient clinic population, it captures a snapshot rather than a trajectory, and it cannot establish that lipid changes precede insulin resistance rather than accompany it. The cohort is drawn from one Turkish hospital network, and the cut-off values identified—106 milligrams per deciliter for triglycerides and 1.96 for the ratio—may require recalibration in populations with different ancestry, diet, and baseline metabolic risk. The HOMA-IR threshold of 2.5, while widely used, is itself a convention rather than a universal biological constant.

Even with those caveats, the message for the public is compelling. Millions of people walk around with undiagnosed insulin resistance, feeling perfectly well while their metabolism quietly drifts toward type 2 diabetes, fatty liver disease, and cardiovascular trouble. This study suggests that the first clue may already be sitting in the blood work ordered at an ordinary checkup. A triglyceride level creeping past 106 milligrams per deciliter, or a TG/HDL-C ratio above roughly 2, is not a diagnosis—but it is a nudge, a cheap and early signal that the conversation about metabolic health should happen now, not after glucose numbers cross the line.

Subject of Research: The association between lipid parameters, the neutrophil-to-lymphocyte ratio, and insulin resistance in non-diabetic individuals

Article Title: Evaluation of the relationship between lipid parameters, neutrophil-to-lymphocyte ratio, and insulin resistance in non-diabetic individuals: a cross-sectional analysis

Article References: Gökdemir, Ö., Aygün, O., & Asma Sakallı, A. (2026). Evaluation of the relationship between lipid parameters, neutrophil-to-lymphocyte ratio, and insulin resistance in non-diabetic individuals: a cross-sectional analysis. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02566-7

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02566-7

Keywords: insulin resistance, triglycerides, TG/HDL-C ratio, neutrophil-to-lymphocyte ratio, HOMA-IR, HDL cholesterol, metabolic syndrome, prediabetes, type 2 diabetes, cross-sectional study, primary care, lipid metabolism