Abstract
Background
Stress hyperglycemia ratio (SHR), calculated using admission glucose and glycated hemoglobin (HbA1c), has emerged as a marker of acute metabolic stress and adverse outcomes. However, its relationship with major geriatric syndromes remains unclear. This study investigated the association between SHR and malnutrition, sarcopenia, and frailty in older adults attending a geriatric outpatient clinic.
Methods
This retrospective cross-sectional study included patients aged ≥ 65 years who underwent comprehensive geriatric assessment between January 2022 and January 2026. SHR was calculated as admission glucose divided by estimated average glucose derived from HbA1c and categorized into quartiles. Malnutrition was assessed using the Mini Nutritional Assessment–Short Form (MNA-SF), probable sarcopenia risk using the SARC-F questionnaire, and frailty using the Clinical Frailty Scale (CFS). Restricted cubic spline analyses and multivariable logistic regression models were performed to evaluate associations between SHR quartiles and geriatric outcomes.
Results
A total of 1,401 older adults were included (median age: 73 years [IQR: 69–78]; 66% female). The median SHR was 0.80 (IQR: 0.73–0.89). Restricted cubic spline analyses demonstrated significant nonlinear associations between SHR and geriatric outcomes, with lower SHR values associated with higher odds of malnutrition, probable sarcopenia, and frailty. In fully adjusted analyses, low SHR remained independently associated with probable sarcopenia (OR: 1.51, 95% CI: 1.02–2.25; p = 0.040) and frailty (OR: 1.62, 95% CI: 1.05–2.50; p = 0.031), whereas the association with malnutrition was no longer significant. Associations were more pronounced among participants without diabetes, particularly for probable sarcopenia (p for interaction = 0.038).
Conclusions
Lower SHR values were associated with increased vulnerability to geriatric syndromes, particularly probable sarcopenia and frailty, in older adults. These findings suggest that SHR may reflect impaired metabolic adaptation and reduced physiological reserve in aging populations. Further prospective studies are needed to establish the clinical utility of SHR as a marker of geriatric vulnerability.
Keywords: Stress hyperglycemia ratio, Sarcopenia, Frailty, Malnutrition, Older adults, Geriatrics
Introduction
Aging is associated with increased prevalence of multimorbidity and functional impairments, which significantly elevate the risk of adverse clinical outcomes in older adults. Especially, malnutrition, sarcopenia, and frailty are highly prevalent in the older population [1, 2]. These syndromes share similar etiological factors, including inflammation, reduced food intake, increased energy requirements, decreased physical activity, and hormonal alterations. Therefore, older adults may experience more than one of these syndromes simultaneously [3].
Malnutrition is a condition resulting from insufficient nutrient intake or absorption, leading to alterations in body composition, impaired physical and cognitive function, and worsened disease outcomes [4]. Older adults are particularly vulnerable to malnutrition due to disease-related catabolism, reduced dietary intake, and age-related physiological changes. Furthermore, malnutrition is recognized as a critical contributing factor within the complex etiology of sarcopenia and frailty [5]. In order to screen older adults for malnutrition, several malnutrition screening instruments, including the Mini Nutritional Assessment-Short Form (MNA-SF), the Nutritional Risk Screening 2002 (NRS-2002), and the Malnutrition Universal Screening Tool (MUST), are commonly used [4, 5].
Sarcopenia is characterized by progressive loss of skeletal muscle mass and strength and is associated with adverse outcomes such as falls, fractures, frailty, and increased mortality [6]. Its prevalence is approximately 10–16% of older adults worldwide [7]. The pathogenesis of sarcopenia is multifactorial and involves inflammation, metabolic dysregulation, and neuromuscular degeneration [8, 9]. The SARC-F questionnaire is commonly used as a rapid screening tool for possible sarcopenia [10]. Frailty, another common geriatric syndrome, is defined as a state of decreased physiological reserve and increased vulnerability to adverse health outcomes. Globally, the prevalence of frailty is approximately 11% among individuals aged 50–59 years, rising to as high as 51% in those aged 90 years and older [11]. In clinical practice, frailty can be assessed using gait speed, the Timed Up and Go (TUG) test, and the Clinical Frailty Scale (CFS) [11, 12].
Stress hyperglycemia refers to a transient elevation in blood glucose levels that occurs during acute medical stress. The onset of hyperglycemia in response to acute stress serves as a physiological compensatory mechanism, designed to fulfill the body’s elevated energy demands to cope with unforeseen physiological insults. The pathogenesis of stress hyperglycemia is driven by the multifaceted interplay of counter-regulatory hormones, including catecholamines, growth hormone, and cortisol, alongside inflammatory mediators such as cytokines. To more accurately evaluate this condition, a novel metric known as the stress hyperglycemia ratio (SHR) has emerged. It is calculated as the ratio of the blood glucose level at hospital admission to the estimated average glucose (derived from glycated hemoglobin, HbA1c) [13]. By adjusting for the influence of long-term chronic baseline glycemic levels, this ratio provides a more precise reflection of acute glycemic fluctuations under stressful conditions [14].
Stress hyperglycemia is associated with adverse clinical outcomes in older adults. Previous studies across various clinical settings in older populations have reported both linear and nonlinear associations between SHR and conditions such as physical frailty among patients with heart failure with preserved ejection fraction [12], delirium [15, 16], hypertension [17], cardiovascular disease risk [18], mortality in patients with severe community-acquired pneumonia [19] and hip fracture [20]. To date, no studies have concurrently examined the association between SHR and geriatric syndromes which are malnutrition, frailty, and sarcopenia among older adults followed in outpatient settings. As these geriatric syndromes share overlapping biological pathways with stress hyperglycemia, including impaired anabolism, inflammatory dysregulation, and nutritional compromise, SHR may not act in isolation but rather converge simultaneously on multiple dimensions of geriatric vulnerability. The present study therefore aimed to investigate the concurrent relationship between SHR and malnutrition, sarcopenia, and frailty in older adults followed in outpatient settings.
Methods
Study design and population
This single-center retrospective cross-sectional study was conducted at the geriatric outpatient clinic of a tertiary university hospital. Patients aged ≥ 65 years who presented to the geriatric medicine outpatient clinic between January 1, 2022, and January 1, 2026, were included in the study.
Eligible participants met the following inclusion criteria: (a) age ≥ 65 years; (b) availability of simultaneously measured blood glucose and HbA1c levels at admission; and (c) availability of data from selected components of a comprehensive geriatric assessment (CGA) performed within the same time frame. These components included malnutrition assessment using the MNA-SF, probable sarcopenia risk assessment using the SARC-F questionnaire, and frailty assessment using the CFS. Patients were excluded if they (a) were receiving hemodialysis, (b) had a history of solid organ transplantation (particularly kidney or liver), (c) had active malignancy, (d) had advanced dementia, or (e) had hemoglobin levels < 10 g/dL. For patients with multiple admissions during the study period, only the first admission was included in the analysis. Patients with missing data in key variables were excluded.
Data collection
All data were retrospectively obtained from the electronic medical records of the geriatric outpatient clinic. Demographic and anthropometric characteristics, including age, sex, educational status, and body mass index (BMI), were recorded. Lifestyle-related variables such as smoking status, alcohol consumption, and living arrangements were also documented. Clinical data included comorbidities, comorbidity burden assessed using the Charlson Comorbidity Index (CCI), and polypharmacy status. Functional status was evaluated using the Katz Activities of Daily Living (ADL) scale [21], and cognitive status was assessed using the Standardized Mini-Mental State Examination (S-MMSE) [22]. Data from the comprehensive geriatric assessment were also collected. Laboratory parameters measured at admission included blood glucose, HbA1c, hemoglobin, albumin, and creatinine levels. All laboratory analyses were performed using standardized methods in the hospital’s central laboratory.
Calculation of the stress hyperglycemia ratio
The SHR was calculated by dividing the admission glucose level (mg/dL) by the estimated average glucose derived from HbA1c using the formula “28.7 × HbA1c (%) − 46.7” [23]. Patients were then categorized into quartiles (Q1–Q4) according to the distribution of SHR values in the study population.
Assessment of malnutrition, sarcopenia and frailty
Malnutrition was assessed using the MNA-SF, a validated screening tool for malnutrition in older adults. The MNA-SF includes items on reduced food intake, recent weight loss, mobility, psychological stress or acute illness, neuropsychological problems, and BMI. Total scores range from 0 to 14. Scores from 12 to 14 indicate normal nutritional status, scores from 8 to 11 indicate risk of malnutrition, and scores from 0 to 7 indicate malnutrition [24].
Probable sarcopenia risk was evaluated using the SARC-F questionnaire, which comprises five components: strength, assistance with walking, rising from a chair, climbing stairs, and history of falls. A total score of ≥ 4 was considered indicative of probable sarcopenia risk [10].
Frailty was assessed using the CFS. CFS scores were assigned by trained clinicians based on patients’ functional status prior to hospital admission, with input from caregivers when necessary. The scale ranges from 1 (very fit) to 9 (terminally ill) [25]. In this study, frailty was defined as a CFS score of ≥ 5.
Statistical analysis
All statistical analyses were performed using IBM SPSS Statistics (Version 27.0; IBM Corp., Armonk, NY, USA) and R software (Version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria). The normality of continuous variables was assessed using the Shapiro–Wilk test and visual inspection of histograms. Continuous variables are presented as mean ± standard deviation (SD) or median with interquartile range (IQR), as appropriate, while categorical variables are presented as frequencies and percentages. Comparisons across SHR quartiles were performed using the Kruskal–Wallis test for continuous variables and the Pearson Chi-square or Fisher’s exact test for categorical variables, as appropriate.
Nonlinear associations between SHR and malnutrition, sarcopenia, and frailty were first explored using univariable restricted cubic spline regression models. Four knots were placed at the 5th, 35th, 65th, and 95th percentiles of SHR, and odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. In the restricted cubic spline analyses, the reference value was set at the median SHR value within the second quartile (Q2), which corresponded to the approximate nadir of the observed risk curves and represented the SHR range associated with the lowest estimated risk. Accordingly, Q2 was used as the reference category in the multivariable logistic regression models to maintain consistency between the spline and categorical analyses. The associations between SHR quartiles and study outcomes were then evaluated using multivariable logistic regression. Three models were constructed: Model 1 (unadjusted), Model 2 (adjusted for age and sex), and Model 3 (adjusted for age, sex, BMI, smoking status, Charlson Comorbidity Index, diabetes mellitus, cognitive impairment [S-MMSE < 24], albumin, and hemoglobin). Covariates were selected based on clinical relevance and previous literature [26–29]. Multicollinearity among covariates was assessed using the variance inflation factor (VIF). No evidence of problematic multicollinearity was identified, with all VIF values below 5. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, while McFadden’s pseudo-R² and the likelihood ratio (overall model) test were used to evaluate the explanatory power and overall significance of each model.
Because diabetes mellitus may influence both the interpretation of SHR and the development of geriatric syndromes, prespecified subgroup analyses were performed according to diabetes status to evaluate the consistency of the observed associations. Interaction effects between SHR and diabetes status were assessed using likelihood ratio tests. A two-sided p value < 0.05 was considered statistically significant.
Results
Baseline characteristics
A total of 1,401 older adults were included in the analysis. The median age was 73 years (IQR: 69–78), and 66% of the participants were female. The median SHR value in the overall population was 0.80 (IQR: 0.73–0.89). The SHR quartiles were defined using the following cut-off values: Q1 (SHR < 0.727), Q2 (0.727 ≤ SHR < 0.798), Q3 (0.798 ≤ SHR < 0.886), and Q4 (SHR ≥ 0.886).
Baseline characteristics stratified by SHR quartiles are presented in Table 1. There were no significant differences across quartiles in terms of age, sex, BMI, or living status. Several clinical and geriatric variables differed significantly across SHR quartiles, including CCI, diabetes prevalence, polypharmacy, and measures of nutritional status (MNA-SF), probable sarcopenia risk (SARC-F), and frailty (CFS) (all p < 0.001).
Table 1.
Baseline characteristics of the study population according to SHR quartiles
| Characteristics | Total (n = 1401) | Q1 (n = 350) | Q2 (n = 352) | Q3 (n = 349) | Q4 (n = 350) | p value |
|---|---|---|---|---|---|---|
| Age (years), median (IQR) | 73 (69–78) | 74 (70–79) | 73 (69–79) | 73 (69–78) | 73 (69–78) | 0.140 |
| Female, n (%) | 924 (66) | 236 (67.4) | 236 (67) | 228 (65.3) | 224 (64) | 0.757 |
| BMI (kg/m2), median (IQR) | 28.63 (25.6-31.94) | 28.44 (25.71–32.04) | 28.91 (25.37–31.99) | 28.89 (25.48–31.64) | 28.53 (25.95–31.64) | 0.940 |
| High school and above, n (%) | 416 (36.1) | 75 (21.4) | 117 (33.2) | 113 (32.4) | 111 (31.7) | 0.001 |
| Living alone, n (%) | 251 (17.9) | 57 (16.3) | 74 (21) | 64 (18.3) | 56 (16) | 0.277 |
| Smoking, n (%) | 342 (24.4) | 103 (29.4) | 70 (19.9) | 90 (25.8) | 79 (22.6) | 0.021 |
| Alcohol use, n (%) | 110 (7.9) | 28 (8) | 32 (9.1) | 29 (8.3) | 21 (6) | 0.472 |
| CCI, median (IQR) | 2 (0–3) | 2 (1–3) | 1 (0–2) | 1 (0–3) | 2 (1–3) | < 0.001 |
| Comorbidities, n (%) | ||||||
| Hypertension | 1025 (73.2) | 249 (71.1) | 250 (71) | 252 (72.2) | 274 (78.3) | 0.094 |
| Diabetes mellitus | 588 (42) | 130 (37.1) | 110 (31.3) | 139 (39.8) | 209 (59.7) | < 0.001 |
| Coronary artery disease | 401 (28.6) | 106 (30.3) | 93 (26.4) | 99 (28.4) | 103 (29.4) | 0.698 |
| Chronic heart failure | 69 (4.9) | 20 (5.7) | 11 (3.1) | 15 (4.3) | 23 (6.6) | 0.156 |
| Chronic kidney disease | 80 (5.7) | 21 (6) | 14 (4) | 22 (6.3) | 23 (6.6) | 0.435 |
| Dementia | 127 (9.1) | 39 (11.1) | 27 (7.7) | 32 (9.2) | 29 (8.3) | 0.403 |
| Polypharmacy, n (%) | 909 (64.9) | 246 (70.3) | 203 (57.7) | 217 (62.2) | 243 (69.4) | < 0.001 |
| KATZ ADL, median (IQR) | 6 (5–6) | 6 (5–6) | 6 (5–6) | 6 (5–6) | 6 (5–6) | < 0.001 |
| S-MMSE < 24 points, n (%) | 286 (20.4) | 92 (26.3) | 67 (19) | 57 (16.3) | 70 (20) | 0.010 |
| MNA-SF, median (IQR) | 13 (11–14) | 12 (11–14) | 13 (12–14) | 13 (12–14) | 13 (11–14) | < 0.001 |
| MNA-SF < 12 points, n (%) | 376 (26.8) | 117 (33.4) | 84 (23.9) | 86 (24.6) | 89 (25.4) | 0.014 |
| SARC-F, median (IQR) | 1 (0–3) | 2 (0–4) | 1 (0–3) | 1 (0–3) | 1 (0–3) | < 0.001 |
| SARC-F ≥ 4 points, n (%) | 329 (23.5) | 116 (33.1) | 71 (20.2) | 69 (19.8) | 73 (20.9) | < 0.001 |
| CFS, median (IQR) | 4 (3–4) | 4 (3–5) | 3 (3–4) | 3 (3–4) | 3 (3–4) | < 0.001 |
| CFS ≥ 5 points, n (%) | 322 (23) | 110 (31.4) | 64 (18.2) | 72 (20.6) | 76 (21.7) | < 0.001 |
| Blood glucose (mg/dL), median (IQR) | 100.08 (88.92-122.04) | 85.86 (79.02–97.02) | 93.06 (87.30-102.06) | 102.96 (95.04-119.52) | 131.94 (108-171.18) | < 0.001 |
| HbA1c (%), median (IQR) | 6 (5.7–6.7) | 6.1 (5.8–6.9) | 5.9 (5.7–6.3) | 5.9 (5.6–6.6) | 6.2 (5.58–7.4) | < 0.001 |
| SHR, median (IQR) | 0.8 (0.73–0.89) | 0.67 (0.63–0.7) | 0.76 (0.75–0.78) | 0.84 (0.82–0.86) | 0.97 (0.93–1.08) | < 0.001 |
| Albumin (g/dL), median (IQR) | 4.31 (4.12–4.5) | 4.23 (4.03–4.39) | 4.33 (4.16–4.5) | 4.35 (4.19–4.52) | 4.36 (4.16–4.56) | < 0.001 |
| Creatinine (mg/dL), median (IQR) | 0.85 (0.71–1.03) | 0.85 (0.7–1.02) | 0.84 (0.7–1.02) | 0.85 (0.71-1) | 0.88 (0.72–1.08) | 0.262 |
| Hemoglobin (g/dL), median (IQR) | 13.6 (12.7–14.6) | 13.4 (12.4–14.3) | 13.6 (12.7–14.6) | 13.7 (12.9–14.7) | 13.8 (12.8–14.8) | < 0.001 |
Abbreviations: ADL activities of daily living, BMI body mass index, CCI Charlson Comorbidity Index, CFS Clinical Frailty Scale, HbA1c glycated hemoglobin, IQR interquartile range, S-MMSE Standardized Mini-Mental State Examination, MNA-SF Mini Nutritional Assessment–Short Form, SHR stress hyperglycemia ratio
SHR quartiles were defined as follows: Q1 (SHR < 0.727), Q2 (0.727 ≤ SHR < 0.798), Q3 (0.798 ≤ SHR < 0.886), and Q4 (SHR ≥ 0.886). Bold values indicate statistical significance (p < 0.05)
Nonlinear associations between SHR and outcomes
Restricted cubic spline analyses demonstrated nonlinear associations between SHR and the risk of malnutrition, probable sarcopenia, and frailty (Fig. 1). Lower SHR values were associated with a higher probability of all three outcomes, whereas no consistent increase in odds was observed at higher SHR levels. The overall associations were statistically significant for malnutrition (p = 0.002), probable sarcopenia (p < 0.0001), and frailty (p = 0.006), with evidence of nonlinearity for all outcomes (p for nonlinearity < 0.001, < 0.0001, and 0.002, respectively).
Fig. 1.
Nonlinear association between stress hyperglycemia ratio (SHR) and the odds of malnutrition, probable sarcopenia, and frailty assessed using univariable restricted cubic spline logistic regression. The blue, red, and green lines represent the odds ratios for malnutrition, probable sarcopenia, and frailty, respectively. Shaded areas indicate 95% confidence intervals (CIs). The dashed horizontal line indicates an odds ratio (OR) of 1. Four knots were placed at the 5th, 35th, 65th, and 95th percentiles of SHR.
Association between SHR quartiles and geriatric syndromes
The associations between SHR quartiles and geriatric syndromes are presented in Table 2. In unadjusted analyses, participants in the lowest SHR quartile (Q1) had higher odds of malnutrition, probable sarcopenia, and frailty compared with the reference group (Q2), and these associations persisted after adjustment for age and sex.
Table 2.
Multivariable logistic regression analysis of the association between SHR quartiles and geriatric syndromes
| SHR quartiles | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | OR (95% CI) | p value | |
| Malnutrition | ||||||
|
Q1 (< 0.727) |
1.60 (1.15–2.23) | 0.005 | 1.56 (1.12–2.19) | 0.010 | 1.27 (0.88–1.84) | 0.205 |
|
Q2 (ref) (≥ 0.727-<0.798) |
1.00 | — | 1.00 | — | 1.00 | — |
|
Q3 (≥ 0.798-<0.886) |
1.04 (0.74–1.47) | 0.810 | 1.09 (0.77–1.55) | 0.635 | 1.09 (0.74–1.59) | 0.667 |
|
Q4 (≥ 0.886) |
1.09 (0.77–1.53) | 0.631 | 1.11 (0.78–1.58) | 0.561 | 1.13 (0.77–1.67) | 0.536 |
| Probable Sarcopenia | ||||||
|
Q1 (< 0.727) |
1.96 (1.39–2.76) | < 0.001 | 1.99 (1.37–2.87) | < 0.001 | 1.51 (1.02–2.25) | 0.040 |
|
Q2 (ref) (≥ 0.727-<0.798) |
1.00 | — | 1.00 | — | 1.00 | — |
|
Q3 (≥ 0.798-<0.886) |
0.98 (0.67–1.41) | 0.895 | 1.06 (0.71–1.57) | 0.787 | 1.04 (0.68–1.58) | 0.867 |
|
Q4 (≥ 0.886) |
1.04 (0.72–1.50) | 0.822 | 1.09 (0.73–1.61) | 0.679 | 0.91 (0.59–1.40) | 0.673 |
| Frailty | ||||||
|
Q1 (< 0.727) |
2.06 (1.45–2.93) | < 0.001 | 2.13 (1.45–3.13) | < 0.001 | 1.62 (1.05–2.50) | 0.031 |
|
Q2 (ref) (≥ 0.727-<0.798) |
1.00 | — | 1.00 | — | 1.00 | — |
|
Q3 (≥ 0.798-<0.886) |
1.17 (0.80–1.70) | 0.413 | 1.34 (0.89–2.01) | 0.156 | 1.34 (0.85–2.12) | 0.212 |
|
Q4 (≥ 0.886) |
1.25 (0.86–1.81) | 0.242 | 1.35 (0.90–2.02) | 0.149 | 1.24 (0.78–1.98) | 0.369 |
Abbreviations: OR odds ratio, CI confidence interval, SHR stress hyperglycemia ratio. Model 1: Crude model. Model 2: Adjusted for age and sex. Model 3: Adjusted for age, sex, BMI, smoking, Charlson Comorbidity Index, diabetes mellitus, cognitive impairment (S-MMSE < 24 points), albumin, hemoglobin
Bold values indicate statistical significance (p < 0.05). McFadden’s pseudo-R² values for the final models (Model 3) were 0.162, 0.216, and 0.305 for malnutrition, probable sarcopenia, and frailty, respectively, with all models demonstrating statistically significant overall model fit (overall model test, p < 0.001). The Hosmer-Lemeshow test indicated adequate model fit for all three final models (Model 3): malnutrition (χ²=5.16, df = 8, p = 0.740), probable sarcopenia (χ²=4.32, df = 8, p = 0.827), and frailty (χ²=6.20, df = 8, p = 0.625)
In the fully adjusted model, low SHR remained independently associated with probable sarcopenia (OR: 1.51, 95% CI: 1.02–2.25; p = 0.040) and frailty (OR: 1.62, 95% CI: 1.05–2.50; p = 0.031), whereas the association with malnutrition was no longer statistically significant (OR: 1.27, 95% CI: 0.88–1.84; p = 0.205). No significant associations were observed for higher SHR quartiles (Q3 and Q4).
Subgroup analyses according to diabetes status
Subgroup analyses stratified by diabetes status are presented in Fig. 2. The association between low SHR and geriatric outcomes differed according to diabetes status. In older patients without diabetes, low SHR (Q1) was significantly associated with higher odds of malnutrition (OR: 1.64, 95% CI: 1.01–2.65; p = 0.044), probable sarcopenia (OR: 2.07, 95% CI: 1.22–3.56; p = 0.008), and frailty (OR: 2.12, 95% CI: 1.19–3.87; p = 0.012). In contrast, no significant associations were observed among participants with diabetes. Interaction analysis showed a statistically significant interaction between SHR and diabetes status for probable sarcopenia (p for interaction = 0.038), but not for malnutrition (p = 0.230) or frailty (p = 0.232).
Fig. 2.
Association between low stress hyperglycemia ratio (SHR) and geriatric outcomes according to diabetes status. Forest plot showing odds ratios (ORs) and 95% confidence intervals (CIs) for malnutrition, probable sarcopenia, and frailty associated with low SHR (Q1 ≤ 0.727), stratified by the presence of diabetes mellitus. Estimates were derived from multivariable logistic regression (Model 3). The second SHR quartile (Q2) served as the reference group. Interaction between SHR and diabetes status was assessed using likelihood ratio tests. Bold values indicate statistical significance (p < 0.05).
Discussion
This study investigated the association between the SHR and geriatric syndromes in older adults attending a geriatric outpatient clinic. We demonstrated a nonlinear association between SHR and geriatric vulnerability, whereby lower SHR values were independently associated with higher odds of probable sarcopenia and frailty. These findings suggest that low SHR in older adults may reflect impaired physiological reserve and reduced capacity to respond to metabolic stress. Although previous studies evaluating SHR have mainly focused on hospitalized or critically ill populations, data regarding outpatient older adults remain scarce. In this context, our findings provide additional evidence supporting a potential relationship between SHR and geriatric vulnerability in outpatient older adults. Given that frailty is characterized by decreased resilience to stressors and multisystem vulnerability, our results support a potential association between impaired glycemic stress response and geriatric syndromes in aging populations.
In the present study, although lower MNA-SF scores were more prevalent in the low SHR group in the baseline analysis, the association was no longer significant after multivariable adjustment. This finding indicates that low SHR is not an independent predictor of malnutrition risk as assessed by the MNA-SF in older adults and suggests that the observed relationship may be largely explained by several coexisting factors, including age-related physiological changes, comorbidity burden, and functional impairment, given that malnutrition is recognized as a multifactorial geriatric syndrome [30]. Similarly, a previous study in trauma patients reported that although both stress-induced hyperglycemia and malnutrition determined by the Geriatric Nutritional Risk Index (GNRI) were independently associated with adverse clinical outcomes, no direct relationship was observed between nutritional status and the incidence of stress hyperglycemia [31]. These findings imply that altered glycemic stress responses and malnutrition may represent distinct, yet partially overlapping, domains of vulnerability in older adults.
In regard to sarcopenia, older adults in the lowest SHR quartile had a 51% higher likelihood of probable sarcopenia, independent of potential confounding factors in our study population. Hyperglycemia has been identified as a risk factor for sarcopenia, contributing to age-related declines in muscle mass and function through pathways involving oxidative stress and chronic inflammation [7, 32]. Our findings further suggest that excessively low SHR values may also be clinically relevant in older adults. In the univariable spline analysis, the association between SHR and probable sarcopenia risk exhibited a U-shaped pattern, indicating that both acute stress hyperglycemia and inadequate stress-related glycemic responses may be linked to sarcopenia. Very low SHR values may indicate an inadequate glycemic response to acute physiological stress relative to chronic glycemic status, potentially representing reduced physiological reserve and impaired metabolic adaptability in older adults.
Frailty was also independently associated with low SHR values in our study population. Similarly, both excessive and inadequate glycemic stress responses may be associated with frailty in older adults. Frailty is characterized by decreased physiological reserve and impaired resilience to stressors, which may partly explain the observed relationship between low SHR and frailty. A recent study reported that elevated SHR was independently associated with poorer physical performance in frail older adults with heart failure with preserved ejection fraction. Notably, spline curve analysis revealed a U-shaped relationship between SHR and gait speed, implying that both acute stress hyperglycemia and chronic disturbances in glucose regulation may contribute to functional decline in frail older adults [12]. These findings suggest that SHR could serve as an indicator of metabolic resilience and stress adaptation capacity in older adults, beyond its conventional role as a marker of stress hyperglycemia.
Low SHR in older adults may also reflect impaired counter-regulatory and autonomic responses rather than the absence of metabolic stress itself. Aging is associated with alterations in autonomic nervous system function and attenuated hormonal responses to stressors [33, 34]. Therefore, low SHR may represent a blunted metabolic stress response and reduced physiological resilience, potentially contributing to sarcopenia and frailty. In the outpatient setting, where acute physiological stress is less likely to be the primary driver of glycemic alterations, SHR may therefore be better interpreted as a marker of impaired metabolic flexibility and physiological reserve rather than acute stress hyperglycemia.
Interestingly, the association between low SHR and geriatric syndromes appeared to be more pronounced among older adults without diabetes, with a statistically significant interaction observed only for probable sarcopenia. One possible explanation is that SHR may more accurately reflect acute metabolic adaptability in individuals without chronic dysglycemia, whereas its interpretation in diabetes may be influenced by long-standing alterations in glucose homeostasis, adaptive metabolic changes, and glucose-lowering therapies.
This study has several limitations. First, due to the retrospective cross-sectional design of the study, causal inferences between SHR and geriatric syndromes cannot be established. Second, the study was conducted in a single geriatric outpatient clinic, which may limit the generalizability of the findings to other populations, including hospitalized or critically ill older adults. Third, probable sarcopenia was assessed using the SARC-F questionnaire, a screening tool, rather than objective measurements of muscle strength or muscle mass, which may have resulted in misclassification. Likewise, malnutrition was evaluated using the MNA-SF, a validated screening instrument, rather than a comprehensive diagnostic assessment. Therefore, our findings should be interpreted in the context of screening-defined outcomes. Fourth, although diabetes mellitus was included in the fully adjusted model, detailed information regarding anti-diabetic treatment regimens, including insulin and oral anti-diabetic medication use, was not fully available and therefore could not be comprehensively accounted for in the analyses. Finally, SHR was originally developed as a marker of stress hyperglycemia in acute illness settings, where acute physiological stressors trigger counter-regulatory hormonal responses and transient increases in glucose levels. Since the present study included outpatient older adults, the presence and degree of acute physiological stress at the time of assessment could not be clearly determined. Therefore, SHR in this population may reflect impaired metabolic flexibility, reduced physiological resilience, or autonomic vulnerability in glucose regulation rather than a pure acute stress response. Despite these limitations, this study is strengthened by its large sample size, comprehensive geriatric assessment, and the simultaneous evaluation of malnutrition, probable sarcopenia, and frailty in relation to SHR in an outpatient geriatric population.
Conclusion
Low SHR was independently associated with probable sarcopenia and frailty in older adults, suggesting that impaired glycemic stress responses may reflect reduced physiological reserve and metabolic resilience in aging populations. Given that SHR can be easily derived from routinely available laboratory parameters, it may have potential as a practical marker to help identify vulnerable older adults who may benefit from further geriatric assessment in routine clinical practice. Future prospective studies incorporating repeated SHR measurements and objective physical performance assessments are needed to establish the clinical utility of SHR as a marker of geriatric vulnerability.
Acknowledgements
The authors acknowledge that preliminary findings of this study were submitted for presentation at the 48th ESPEN (The European Society for Clinical Nutrition and Metabolism) Annual Congress (Berlin, Germany, 5-8 September 2026).
Abbreviations
- ADL
Activities of Daily Living
- BMI
Body Mass Index
- CCI
Charlson Comorbidity Index
- CFS
Clinical Frailty Scale
- CI
Confidence Interval
- CGA
Comprehensive Geriatric Assessment
- HbA1c
Glycated Hemoglobin
- IQR
Interquartile Range
- MNA-SF
Mini Nutritional Assessment–Short Form
- OR
Odds Ratio
- RCS
Restricted Cubic Spline
- SARC-F
Strength, Assistance with Walking, Rising from a Chair, Climbing Stairs, and Falls Questionnaire
- SD
Standard Deviation
- SHR
Stress Hyperglycemia Ratio
- S-MMSE
Standardized Mini-Mental State Examination
- VIF
Variance Inflation Factor
Authors’ contributions
Tubanur Kocaaslan: conceptualization, methodology, investigation, writing-original draft. Ugur Balaban: conceptualization, methodology, formal analysis, writing-original draft. Okan Turhan: resources, writing-review editing. Burcu Kelleci Cakir: methodology, investigation, writing-review editing. Mert Esme: resources, writing-review editing. Burcu Balam Dogu: resources, writing-review editing. Meltem Gulhan Halil: resources, writing-review editing. Mustafa Cankurtaran: resources, writing-review editing. Cafer Balci: investigation, methodology, resources, writing-review editing. All authors read and approved the submitted version of the manuscript.
Funding
None.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki and was approved by the Hacettepe University Health Sciences Research Ethics Committee (No: SBA 26/161). Informed consent was waived due to the retrospective nature of the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.