Altitude‐Related Laboratory Changes and a Novel Hemoglobin Estimation Model: A Retrospective Analysis in Children and Adults

ABSTRACT

Introduction

Hypobaric hypoxia at higher residential altitudes may modify routine laboratory parameters and complicate their interpretation in emergency care. We examined associations between altitude (0–2500 m) and laboratory findings and developed a hemoglobin estimation model.

Methods

This single‐center, retrospective cross‐sectional study included 2302 patients aged ≥ 1 year admitted to the Emergency Department of Kars Harakani State Hospital on January 1–3 and July 1–3, 2024. Patients were categorized as children (1–17 years) or adults (≥ 18 years). Hematological and biochemical results were retrieved from electronic records. Altitude–laboratory associations were analyzed using correlation and linear regression. A multivariable regression model was constructed to identify determinants of hemoglobin variation.

Results

Among 2302 admissions, 30.8% were pediatric. Hemoglobin, hematocrit, erythrocyte indices, pO₂, and SaO₂ demonstrated the most consistent altitude‐related patterns. In adults, altitude showed positive associations with glucose, urea, creatinine, COHb, MetHb, calcium, ALT, AST, bilirubin, and selected erythrocyte and platelet indices, and negative associations with SaO₂, pO₂, PLT, PCT, GFR, and HCO₃ (all p < 0.05). Most parameters were not significantly associated with altitude in children. SaO₂ and pO₂ were lower in summer than winter (p < 0.05). The final model explained 83% of hemoglobin variability, incorporating altitude, age, sex, smoking, pregnancy, malignancy, and chronic disease.

Conclusion

Residential altitude was associated with measurable variation in hematological, biochemical, and oxygenation parameters among emergency department patients. Altitude should be considered when interpreting laboratory results in clinical practice. The proposed hemoglobin model integrates altitude and clinical variables to characterize hemoglobin variability within this cohort.

Residential altitude, even within a low‐to‐moderate range, was associated with measurable variation in routine laboratory profiles among emergency department patients, most prominently in adults. Altitude‐related patterns involved hematologic indices, biochemical markers, and arterial oxygenation parameters. A multivariable hemoglobin estimation model integrating altitude with demographic and clinical variables explained 83% of hemoglobin variability, supporting altitude‐aware interpretation of laboratory results and emphasizing the need for external validation in broader emergency care populations across diverse clinical settings.

1. Introduction

Throughout human history, settlements have been established at various altitudes, but today, the majority of the world's population lives in areas between 0 and 1000 m. However, there are also permanent settlements at altitudes of 4000 m and above. Altitudes are defined in the literature as low (0–1500 m), medium (1500–2500 m), high (2500–4000 m), very high (4000–5500 m), or extremely high (above 5500 m) relative to sea level [1, 2]. Increasing altitude leads to decreasing atmospheric pressure, thus reducing the partial pressure of oxygen in the air and causing hypobaric hypoxia [1, 3, 4].

Hypobaric hypoxia triggers pathophysiological processes and adaptation mechanisms in the human body, especially causing changes in hematological and biochemical parameters. The lack of oxygen in the atmosphere causes a decrease in pO₂ in blood gas [1, 2, 5]. This affects many laboratory parameters, including hemoglobin, and complicates the clinical evaluation process [1, 2, 3, 4]. The normal reference ranges for routine laboratory tests are not always consistent in individuals living at different altitudes [6]. These changes are observed both in adults and children [7, 8].

Using uniform reference ranges in countries with varying altitudes, such as Turkey, can lead to erroneous results in clinical evaluations [9]. Acute changes in laboratory values are particularly critical for both diagnosis and prognosis in emergency departments. Therefore, altitude may represent an important contextual factor alongside the patient's clinical history and findings. However, this factor is often not explicitly considered in routine clinical practice, which may contribute to challenges in interpretation. Understanding the response mechanisms associated with hypobaric hypoxia necessitates accurate assessment of altitude‐affected reference values in both adult and pediatric patients.

Although many studies in the literature have investigated the effects of altitude on metabolism, the majority of these studies have focused on altitudes of ≥ 3000 m [10, 11]. Furthermore, current studies generally focus on pathological conditions such as acute or chronic mountain sickness without adequately explaining altitude‐related asymptomatic effects [12, 13, 14, 15]. Consequently, studies evaluating laboratory findings in people living at 0–2500 m who do not have any altitude‐related illness/complaints are quite limited; more data are needed in this area.

In this study, relationships between laboratory parameters and altitude were comprehensively examined in individuals residing at altitudes ranging from 0 to 2500 m. We aimed to investigate altitude‐associated variation in laboratory parameters across pediatric and adult age groups within a real‐world emergency department population, to provide an altitude‐informed perspective for the interpretation of laboratory findings in clinical practice, and to derive an exploratory regression‐based model describing factors associated with hemoglobin variation.

2. Methods

2.1. Study Design and Setting

This was a single‐center, retrospective, cross‐sectional observational study. It was performed at the Emergency Department of Kars Harakani State Hospital, which is located at 1768 m altitude and accepts patients from surrounding settlements between 0 and 2500 m, from January 1–3, 2024 (winter season) to July 1–3, 2024 (summer season). A total of 4512 patients who visited the emergency department during these dates were screened.

Because the study was conducted exclusively in an emergency department setting, the cohort was not intended to represent a healthy physiological reference population. Rather, it reflects a heterogeneous clinical population with varying acute and chronic medical conditions, allowing assessment of altitude‐associated laboratory variation within a real‐world emergency care context.

2.2. Inclusion Criteria

• Laboratory tests within the scope of the study must have been performed and recorded at the time of admission.

• Information on age, sex, smoking status, chronic disease status, and residence must be available in clinical records.

• Being ≥ 1 year old.

2.3. Exclusion Criteria

• Diagnosis of acute or chronic bleeding.

• Chronic hematological disease that may affect hematological parameters (e.g., aplastic anemia, and polycythemia vera).

• Active chemotherapy or drug use that may affect the hematological series.

• Missing clinical or laboratory data.

2.4. Subgrouping

The patients were divided into two main groups on the basis of age: children (1–17 years) and adults (≥ 18 years). Because of the insufficient number of admissions in the 0–1 age range, the lower age limit for the study was set at 1 year. The child group was further divided into three groups: 1–6 years (n = 336), 7–12 years (n = 207), and 13–17 years (n = 166).

2.5. Variables

Information on patients' altitude of residence, age, sex, smoking status, pregnancy status, degree of malignancy, and history of chronic diseases was recorded. Laboratory data, including hematological values, biochemical tests, coagulation indicators, and arterial blood gas measurements, were collected.

2.6. Laboratory Analyses and Data Acquisition

Biochemical analyses were performed using the Cobas c 501 analyzer (Roche Diagnostics, Basel, Switzerland), hematological parameters using the BC‐6000 hematology analyzer (Mindray Medical International, Shenzhen, China), coagulation tests using the Novae II coagulation analyzer (Tokra Medikal, Istanbul, Turkey), and arterial blood gas analyses, including pO₂, SaO₂, O₂Hb, HHb, COHb, MetHb, HCO₃ ⁻, and base excess measurements, using the ABL800 Basic analyzer (Radiometer Medical, Copenhagen, Denmark).

Patient data, including laboratory results, clinical characteristics, and residential addresses, were obtained from the SisoHbys v2.0.5.112 Sisoft Healthcare Information System (SISOFT, Ankara, Turkey). Clinical features were extracted from discharge summaries, and addresses were verified with patients if records were missing or inconsistent. In addition, residence at the recorded address for at least 2 weeks prior to emergency department admission was confirmed during the address verification process to reduce potential misclassification arising from recent relocation or short‐term travel and to better characterize residential altitude exposure. The altitude of each residence was determined via online topography databases [16].

2.7. Statistical Analysis

Data analysis was performed via IBM SPSS Statistics v27.0 (IBM Corp., Armonk, NY, USA), R v4.5.1 (R Foundation, Kaysville, UT, USA), and RStudio v2025.05.1‐513 (Posit PBC, Boston, MA, USA). Continuous variables are presented as the mean ± standard deviation (SD) or median (minimum–maximum), whereas categorical variables are presented as the number (n) and percentage (%).

The associations between altitude and laboratory variables were examined via Pearson or Spearman correlation analysis. Univariate linear regression analyses were performed to predict hemoglobin levels; the significant variables were included in multivariate models to create five different regression models. Model suitability was evaluated via R ², adjusted R ², the Akaike information criterion (AIC), and Bayesian information criterion (BIC) tests; multicollinearity was tested via the variance inflation factor (VIF). Model error metrics were calculated as the mean absolute error (MAE) and root mean square error (RMSE), and generalizability was tested via 10‐fold cross‐validation.

To enhance interpretability of the regression results, the coefficients of the final multivariable model were rounded to the nearest integers, allowing a simplified representation of the modeled relationships between hemoglobin levels and the included variables. A constant term was retained based on the original regression equation, and the uncertainty of the estimates was expressed using ±95% confidence intervals. This simplified representation was intended to illustrate the direction and relative magnitude of associations identified in the exploratory regression analysis, while acknowledging that further validation would be required before broader clinical application. Statistical significance was set at p < 0.05.

3. Results

3.1. Descriptives

A total of 4512 emergency department visits were evaluated; 2302 (51.0%) patients were included in the analysis as eligible. According to the seasonal distribution, 1075/1974 visits (54.5%) in summer and 1227/2538 visits (48.3%) in winter were included for analysis. The study group consisted of 1593 adults (69.2%) and 709 children (30.8%). The children were divided into three groups: 1–6 years old (n = 336), 7–12 years old (n = 207), and 13–17 years old (n = 166). The mean cohort age was 31.9 ± 0.5 years; that of adults alone was 42.7 ± 18.6 years. The male rate was 43.2% in the general population, 53% in children, and 38.4% in adults. The mean reported residential altitude was 1551 ± 12 m for all, 1595 ± 555 m for children, and 1521 ± 611 m for adults; the minimum altitude was 3, and the maximum altitude was 2556 m.

Chronic comorbid conditions were observed more frequently in adults than in children. Chronic hypoxic pulmonary diseases accounted for 10.4% of the population; the most common chronic hypoxic pulmonary diseases were chronic obstructive pulmonary disease (3.7%), asthma (3.6%), and chronic bronchitis (1.4%). Hypertension (11.6%), diabetes (8.5%), and ischemic heart disease (4.5%) occurred most frequently in adults. In children, comorbidities are rare, with epilepsy and congenital heart disease having the highest prevalence. The malignancy rate in the general population is 3.1%, almost all of which are adults. Smoking was observed only in adults (20.3%), and the pregnancy rate was 12.2%. All descriptive characteristics are presented in Table 1.

TABLE 1.

Demographic and clinical characteristics of the study population by age groups.

	Total (n = 2302)	Adult (n = 1593)	Child (n = 709)
			1–6 years (n = 336)	7–12 years (n = 207)	13–17 years (n = 166)
Age (years)	31.9 ± 0.5	42.7 ± 18.6	3.1 ± 1.7	9.1 ± 1.8	15.6 ± 1.4
Sex (male)	994 (43.2)	612 (38.4)	178 (53)	116 (56)	88 (53)
Residence altitude (m)	1551 ± 12	1521 ± 611	1595 ± 555	1672 ± 513	1603 ± 566
Admission season (winter)	1227 (53.3)	799 (50.9)	207 (61.6)	143 (69.1)	78 (47)
Comorbidities
Chronic hypoxic pulmonary diseases	240 (10.4)	230 (14.4)	2 (0.6)	3 (1.4)	5 (3)
COPD	86 (3.7)	86 (5.4)	—	—	—
Asthma	83 (3.6)	78 (4.9)	1 (0.3)	1 (0.5)	3 (1.8)
Chronic bronchitis	33 (1.4)	33 (2.1)	—	—	—
Intersititial lung disease	17 (0.7)	17 (1.1)	—	—	—
Bronchiectasis	10 (0.4)	10 (0.6)	—	—	—
Tuberculosis	7 (0.3)	7 (0.4)	—	—	—
Pulmonary hypertension	5 (0.2)	5 (0.3)	—	—	—
Chronic kidney disease	92 (4)	92 (5.8)	—	—	—
Other chronic diseases	555 (24.1)	529 (33.2)	9 (2.7)	9 (4.3)	8 (4.8)
Hypertension	184 (8)	184 (11.6)	—	—	—
Diabetes	144 (6.3)	136 (8.5)	2 (0.6)	4 (1.9)	2 (1.2)
Ischemic heart disease	72 (3.1)	72 (4.5)	—	—	—
Thyroidits	71 (3.1)	71 (4.5)	—	—	—
Dyslipidemia	50 (2.2)	50 (3.1)	—	—	—
Heart failure	42 (1.8)	42 (2.6)	—	—	—
Dysrhytmia	41 (1.8)	41 (2.6)	—	—	—
Migraine	33 (1.4)	33 (2.1)	—	—	—
Epilepsy	16 (0.7)	10 (0.6)	3 (0.9)	2 (1)	1 (0.6)
SLE	15 (0.7)	15 (0.9)	—	—	—
Rheumatoid arthiritis	15 (0.7)	15 (0.9)	—	—	—
Inflammatory bowel disease	13 (0.6)	12 (0.8)	—	—	1 (0.6)
Gastritis	12 (0.5)	12 (0.8)	—	—	—
FMF	7 (0.3)	5 (0.3)	—	—	2 (1.2)
Gout	6 (0.3)	6 (0.4)	—	—	—
Congenital heart disease	6 (0.3)	1 (0.1)	2 (0.6)	2 (1)	1 (0.6)
Cerebrovascular disease	5 (0.2)	5 (0.3)	—	—	—
Other	65 (2.8)	56 (3.5)	3 (0.9)	3 (1.4)	3 (1.8)
Malignancy	71 (3.1)	70 (4.4)	1 (0.3)	—	—
Pregnancy	194 (8.4)	194 (12.2)	—	—	—
Smoking	324 (14.1)	324 (20.3)	—	—	—

Note: Values are presented as the mean ± standard deviation for continuous variables and n (%) of group for categorical variables. “Adult” group includes individuals aged ≥ 18 years; “Child” group includes individuals aged 1–17 years, further stratified into 1–6, 7–12, and 13–17 years subgroups. “Residence Altitude” refers to the elevation (in meters) of each participant's place of residence. Comorbidities are shown as frequencies and percentages within respective age groups. “Other Chronic Diseases” includes various less common conditions not listed individually. Emdash (—) indicates no cases were observed in that subgroup.

Abbreviations: COPD, chronic obstructive pulmonary disease; FMF, Familial Mediterranean Fever; SLE, systemic lupus erythematosus.

3.2. Laboratory Findings

Considering all emergency department visits (n = 4512), the laboratory tests with the highest rates were glucose (50.8%), creatinine (50.6%), C‐reactive protein (CRP) (50.8%), and hematological parameters (51.0%). Hemoglobin, hematocrit, glucose, urea, creatinine, and C‐reactive protein (CRP) levels were higher in adults, whereas white blood cell (WBC) count, lymphocyte percentage (LYM%), glomerular filtration rate (GFR), and alkaline phosphatase (ALP) levels were higher in children (p < 0.05). Coagulation tests (aPTT, PT, and INR) and blood gas parameters were not significantly different between age groups (p > 0.05). Observed hemoglobin values ranged from 8.1–19.3 g/dL in adults to 8.1–19.4 g/dL in children; hematocrit ranged from 24%–58.8% in adults to 23.7%–58.7% in children; pO₂ ranged from 62.1–119 mmHg in adults to 65.1–125 mmHg in children; and SaO₂ ranged from 64.1%–99.7% in adults to 63.3%–99.8% in children. The minimum and maximum values did not significantly differ according to age (Table 2).

TABLE 2.

Laboratory descriptive statistics.

Parameters	Total (n = 2302)		Adult (n = 1593)	Child (n = 709)
	α	β		1–6 years (n = 336)	7–12 years (n = 207)	13–17 years (n = 166)
Glucose (mg/dL)	106.9 ± 37.5	2294 (50.8)	110.8 ± 42.4	96.3 ± 19.5	100.9 ± 26.0	99.2 ± 15.3
Urea (mg/dL)	30.5 ± 17.8	2257 (50.0)	33.0 ± 19.7	23.4 ± 13.3	26.1 ± 8.4	26.2 ± 6.8
Creatinine (mg/dL)	0.8 ± 0.5	2282 (50.6)	0.9 ± 0.6	0.4 (0.2–6.2)	0.5 (0.3–6.1)	0.7 ± 0.3
GFR (mL/min/1.73 m2)	101.9 ± 22.8	1873 (41.5)	100.6 ± 24.4	107.5 ± 9.4	106.5 ± 13.6	109.9 ± 15.4
CRP (mg/L)	5 (0.3–379)	2290 (50.8)	5.1 (0.3–378)	5.4 (0.3–255)	4.5 (0.3–151)	3.1 (0.4–343)
Albumin (g/dL)	4.4 ± 0.4	2205 (48.9)	4.3 ± 0.4	4.4 ± 0.3	4.5 ± 0.3	4.6 ± 0.3
Total protein (g/dL)	7.2 ± 0.5	2180 (48.3)	7.2 ± 0.5	6.8 ± 0.5	7.2 ± 0.5	7.4 ± 0.5
Na+ (mmol/L)	137.8 ± 2.9	2295 (50.9)	138.0 ± 2.8	136.9 ± 3.3	137.0 ± 2.6	138.3 ± 2.6
K+ (mmol/L)	4.4 ± 0.5	2279 (50.5)	4.4 ± 0.5	4.5 ± 0.6	4.3 ± 0.5	4.4 ± 0.5
Ca2+ (mg/dL)	9.3 ± 0.5	2278 (50.5)	9.1 ± 0.5	9.7 ± 0.5	9.5 ± 0.5	9.5 ± 0.4
ALT (U/L)	16 (5–589)	2298 (50.9)	17 (5–589)	15 (6–246)	13 (5–363)	15.8 ± 8.4
AST (U/L)	22 (8–595)	2293 (50.8)	20 (8–595)	36.9 ± 22.0	27.1 ± 22.0	21.7 ± 6.8
GGT (U/L)	14 (3–1180)	2129 (47.2)	17 (3–1180)	10 (4–370)	10 (4–188)	10 (4–417)
ALP (U/L)	134.1 ± 91.4	2122 (47.0)	96.9 ± 55.6	233.7 ± 91.5	233.8 ± 89.7	178.8 ± 114.3
LDH (U/L)	272.5 ± 97.3	2052 (45.5)	266.6 ± 102.6	310.6 ± 83.7	265.6 ± 68.1	259.1 ± 80.7
Amylase (U/L)	64.4 ± 45.3	2041 (45.2)	68.1 ± 50.9	49.9 ± 30.7	61.2 ± 21.6	61.8 ± 19.8
Lipase (U/L)	26 (7–2053)	1947 (43.2)	30 (8–2053)	22.3 ± 15.6	24.5 ± 19.3	24.8 ± 10.7
T. Bilirubin (mg/dL)	0.4 (0.1–31.5)	2201 (48.8)	0.4 (0.1–31.5)	0.26 (0.1–12.5)	0.4 ± 0.25	0.44 (0.15–10.2)
D. Bilirubin (mg/dL)	0.2 (0.01–6.7)	2201 (48.8)	0.1 (0.02–6.7)	0.14 ± 0.09	0.13 ± 0.07	0.16 ± 0.09
aPTT (s)	30.2 ± 8.4	1864 (41.3)	29.9 ± 8.8	31.1 ± 8.6	30.7 ± 5.6	30.9 ± 5.3
PT (s)	13.6 ± 3.5	1893 (42.0)	13.3 ± 3.4	15.2 ± 4.3	13.5 ± 3.3	13.5 ± 2.0
INR	1.1 ± 0.7	1894 (42.0)	1.1 ± 0.8	1.2 ± 0.4	1.1 (0.6–15.5)	1.1 ± 0.1
pH	7.39 ± 0.05	2051 (45.4)	7.38 ± 0.05	7.40 ± 0.06	7.39 ± 0.05	7.37 ± 0.05
pO2 (mmHg)	80.8 ± 7.6	2051 (45.5)	80.7 ± 7.3	82.7 ± 8.9	80.1 ± 7.2	79.1 ± 6.1
pCO2 (mmHg)	38.1 ± 8.1	2051 (45.5)	40.0 ± 7.6	30.2 ± 5.4	35.7 ± 7.7	40.4 ± 6.8
SaO2 (%)	83.2 ± 7.5	2051 (45.5)	82.8 ± 7.3	85.8 ± 7.7	83.9 ± 7.6	80.8 ± 7.2
O2Hb (%)	81.5 ± 7.6	2051 (45.5)	80.9 ± 7.3	84.5 ± 7.7	82.6 ± 7.6	79.6 ± 7.4
HHb (%)	15.3 ± 7.4	2051 (45.5)	15.7 ± 7.3	12.7 ± 7.8	15.1 ± 7.5	17.5 ± 7.4
COHb (%)	1.3 (0.1–27.3)	2051 (45.5)	1.4 (0.1–27.3)	1.4 ± 0.5	1.1 ± 0.8	1.1 (0.1–22.8)
MetHb (%)	1.3 ± 1.1	2051 (45.5)	1.3 ± 1.1	1.4 ± 1.0	1.2 ± 0.9	1.3 ± 0.9
HCO3 − (mmol/L)	21.9 ± 2.4	2051 (45.5)	22.5 ± 2.3	19.9 ± 2.2	21.1 ± 1.8	21.9 ± 2.0
BE (mmol/L)	−2.2 ± 3.5	2051 (45.5)	−1.3 ± 3.2	−5.9 ± 3.1	−3.5 ± 2.7	−1.6 ± 2.7
Lactate (mmol/L)	1.6 ± 0.9	2051 (45.5)	1.6 ± 0.9	1.9 ± 0.8	1.7 ± 0.6	1.6 ± 0.8
RBC (×106/μL)	4.99 ± 0.6	2299 (51.0)	4.98 ± 0.6	4.83 ± 0.5	5.1 ± 0.4	5.1 ± 0.4
Hb (g/dL)	14.0 ± 1.8	2299 (51.0)	14.2 ± 1.9	12.9 ± 1.5	13.8 ± 1.2	14.6 ± 1.8
HCT (%)	41.5 ± 5.3	2299 (51.0)	42.4 ± 5.4	37.7 ± 4.2	40.1 ± 3.1	42.4 ± 4.8
MCV (fL)	83.4 ± 7.1	2299 (51.0)	85.1 ± 6.5	78.2 ± 7.6	79.6 ± 4.7	82.6 ± 6.7
MCH (pg)	27.9 ± 2.8	2299 (51.0)	28.4 ± 2.6	26.0 ± 3.0	26.8 ± 2.0	27.8 ± 2.8
MCHC (g/dL)	33.4 ± 1.3	2299 (51.0)	33.4 ± 1.3	33.2 ± 1.4	33.7 ± 1.0	33.6 ± 1.2
RDW‐CV (%)	14.1 ± 1.6	2299 (51.0)	14.0 ± 1.6	14.6 ± 1.7	13.8 ± 1.1	14.1 ± 1.9
PLT (×103/μL)	261.7 ± 82.5	2299 (51.0)	244.1 ± 70.4	319.1 ± 105.7	296.7 ± 78.3	270.5 ± 74.8
MPV (fL)	9.5 ± 1.2	2299 (51.0)	9.7 ± 1.2	8.6 ± 1.1	8.9 ± 1.0	9.5 ± 1.2
PCT (%)	0.24 ± 0.01	2299 (51.0)	0.24 ± 0.06	0.26 ± 0.09	0.26 ± 0.06	0.25 ± 0.06
PDW (%)	16.1 ± 0.5	2299 (51.0)	16.2 ± 0.5	15.7 ± 0.5	15.8 ± 0.4	16.1 ± 0.5
WBC (×103/μL)	9.5 ± 3.7	2299 (51.0)	9.0 ± 3.2	11.2 ± 4.8	10.3 ± 4.5	9.7 ± 4.1
LYM# (×103/μL)	2.5 ± 1.5	2299 (51.0)	2.2 ± 0.9	4.2 ± 2.3	2.7 ± 1.4	2.3 ± 1.0
LYM%	28.2 ± 14.0	2299 (51.0)	25.7 ± 10.8	40.3 ± 19.6	29.1 ± 15.7	26.9 ± 13.0
NEU# (×103/μL)	6.2 ± 3.5	2299 (51.0)	6.1 ± 3.0	6.0 ± 4.2	6.8 ± 4.4	6.6 ± 4.1
NEU%	63.1 ± 15.5	2299 (51.0)	65.7 ± 12.4	50.4 ± 20.9	62.4 ± 17.4	64.7 ± 14.8
MON# (×103/μL)	0.6 ± 0.3	2299 (51.0)	0.5 ± 0.2	0.8 ± 0.4	0.6 ± 0.3	0.6 ± 0.3
MON%	6.4 ± 2.3	2299 (51.0)	6.1 ± 2.0	7.6 ± 3.2	6.3 ± 2.2	6.3 ± 2.3
EOS# (×103/μL)	0.11 (0.01–2.3)	2299 (51.0)	0.11 (0.01–1.84)	0.08 (0.01–1.03)	0.09 (0.01–2.3)	0.1 (0.01–1.8)
EOS%	1.2 (0.1–85)	2299 (51.0)	1.4 (0.1–85)	0.8 (0.1–9.6)	1.0 (0.1–23.4)	1.2 (0.1–19.3)
BAS# (×103/μL)	0.02 (0.01–7.1)	2299 (51.0)	0.02 (0.01–7.1)	0.01 (0.01–0.1)	0.01 (0.01–2.6)	0.02 (0.01–0.16)
BAS%	0.2 (0.1–75.2)	2299 (51.0)	0.2 (0.1–75.2)	0.1 (0.1–1)	0.1 (0.1–60.7)	0.2 (0.1–1.6)

Note: Data were presented as the mean ± SD or median (minimum–maximum) according to their distribution patterns. “α” represents the mean or median value obtained in patients who underwent the relevant test, while “β” represents the number of patients who underwent the test and their proportion of the total number of admissions (n = 4512).

Seasonal laboratory findings: The mean temperatures on summer and winter admission days were 14.7°C and −5°C, respectively. In summer, the mean SaO₂ was 81.8% ± 8.1%, and the pO₂ was 79.9 ± 8.0 mmHg, whereas in winter, the SaO₂ was 84.6% ± 6.5%, and the pO₂ was 81.7 ± 7.0 mmHg. Both parameters were significantly lower in the summer (p < 0.001). There was no significant difference in hemoglobin levels between seasons (p > 0.05). The mean blood urea values were 32.2 ± 18.8 mg/dL in summer and 29.0 ± 16.8 mg/dL in winter; the GFR was 97.9 ± 25.2 mL/min/1.73 m² in summer and 104.7 ± 20.5 mL/min/1.73 m² in winter. Urea was significantly greater in summer, and the GFR was significantly greater in winter (p < 0.05 and p < 0.001, respectively). Alkaline phosphatase (ALP) levels were significantly higher in winter (143.9 ± 100.5 U/L) than in summer (122.8 ± 78.4 U/L) (p < 0.001).

3.3. Correlation Between Altitude and Laboratory Parameters

In adults, altitude showed statistically significant associations with several laboratory parameters (p < 0.05). Specifically, mean platelet volume (MPV), glucose, urea, and creatinine were positively associated with altitude, whereas platelet count (PLT), plateletcrit (PCT), and glomerular filtration rate (GFR) showed negative associations. Erythrocyte‐related indices (hemoglobin, hematocrit, MCV, MCH, MCHC, and RDW) demonstrated positive associations with altitude, while oxygenation parameters (pO₂ and SaO₂) were negatively associated.

The pediatric subgroups were analyzed separately according to age. PLT, PCT, and GFR were negatively correlated; creatinine, hemoglobin, HCT, MCV, MCH, MCHC, and RDW were positively correlated with altitude in the 13–17 age group (p < 0.05). The oxygenation parameters pO₂ and SaO₂ were negatively correlated with altitude in the entire child group aged 1–17 years (p < 0.05). Except for pO₂ and SaO₂, no other parameters were significantly correlated with altitude in either the 1–6 or the 7–12 year age groups (p > 0.05) (Table 3).

TABLE 3.

Association of laboratory parameters with altitude.

Parameters	Adult		Child
			1–6 years		7–12 years		13–17 years
	p	r	p	r	p	r	p	r
Glucose	0.008*	0.067	0.74*	—	0.17*	—	0.39*	—
Urea	0.019*	0.059	0.12*	—	0.48*	—	0.91*	—
Creatinine	0.032*	0.054	0.32#	—	0.06#	—	0.03*	0.170
GFR	< 0.001*	−0.202	0.14*	—	0.93*	—	0.01*	−0.285
CRP	0.32#	—	0.92#	—	0.46#	—	0.1#	—
Albumin	0.17*	—	0.11*	—	0,52*	—	0,82*	—
Total protein	0.11*	—	0.59*	—	0.79*	—	0.32*	—
Na+	0.09*	—	0.75*	—	0.49*	—	0.09*	—
K+	0.07*	—	0.11*	—	0.70*	—	0.3*	—
Ca2+	< 0.001*	0.125	0.69*	—	0.24*	—	0.88*	—
ALT	< 0.001 #	0.169	0.29#	—	0.36#	—	0.57*	—
AST	< 0.001 #	0.140	0.15*	—	0.68*	—	0.09*	—
GGT	0.15#	—	0.93#	—	0.84#	—	0.1#	—
ALP	0.17*	—	0.731*	—	0.49*	—	0.42*	—
LDH	0.16*	—	0.91*	—	0.17*	—	0.39*	—
Amylase	0.14*	—	0.56*	—	0.63*	—	0.3*	—
Lipase	0.59#		0.98*	—	0.85*	—	0.85*	—
T. Bilirubin	< 0.001 #	0.183	0.89#	—	0.07*	—	0.32#	—
D. Bilirubin	0.016 #	0.062	0.36*	—	0.1*	—	0.9*	—
aPTT	0.34*	—	0.58*	—	0.29*	—	0.88*	—
PT	0.16*	—	0.98*	—	0.74*	—	0.67*	—
INR	0.13*	—	0.15*	—	0.62#	—	0.38*	—
pH	0.34*	—	0.57*	—	0.62*	—	0.33*	—
pO2	< 0.001*	−0.403	0.022*	−0.130	0.004*	−0.214	0.006*	−0.225
pCO2	0.29*	—	0.71*	—	0.33*	—	0.33*	—
SaO2	0.007*	−0.072	0.031*	−0.122	0.02*	−0.174	0.02*	−0.193
O2Hb	0.96*	—	0.32*	—	0.97*	—	0.37*	—
HHb	0.08*	—	0.28*	—	0.96*	—	0.58*	—
COHb	< 0.001 #	0.101	0.44*	—	0.70*	—	0.54#	—
MetHb	0.007*	0.072	0.63*	—	0.25*	—	0.49*	—
HCO3 −	< 0.001*	−0.176	0.58*	—	0.15*	—	0.83*	—
BE	< 0.001*	−0.107	0.74*	—	0.27*	—	0.67*	—
Lactate	0.23*	—	0.56*	—	0.38*	—	0.29*	—
RBC	< 0.001*	0.365	0.60*	—	0.75*	—	0.3*	—
Hb	< 0.001*	0.662	0.45*	—	0.98*	—	< 0.001*	0.255
HCT	< 0.001*	0.489	0,50*	—	0.94*	—	< 0.001*	0.258
MCV	< 0.001*	0.237	0.96*	—	0.58*	—	< 0.001*	0.276
MCH	< 0.001*	0.288	0.79*	—	0.48*	—	< 0.001*	0.293
MCHC	< 0.001*	0.254	0.14*	—	0.89*	—	0.006*	0.211
RDW—CV	< 0.001*	0.109	0.55*	—	0.64*	—	0.007*	0.211
PLT	< 0.001*	−0.153	0.77*	—	0.59*	—	0.015*	−0.189
MPV	< 0.001*	0.103	0.60*	—	0.33*	—	0.69*	—
PCT	< 0.001*	−0.130	0.23*	—	0.87*	—	0.033*	−0.166
PDW	< 0.001*	0.100	0.98*	—	0.61*	—	0.23*	—
WBC	0.70*	—	0.57*	—	0.07*	—	0.14*	—
LYM#	0.35*	—	0.37*	—	0.33*	—	0.38*	—
LYM%	0.13*	—	0.56*	—	0.69*	—	0.80*	—
NEU#	0.31*	—	0.24*	—	0.16*	—	0.31*	—
NEU%	0.13*	—	0.57*	—	0.74*	—	0.89*	—
MON#	0.41*	—	0.51*	—	0.29*	—	0.16*	—
MON%	0.54*	—	0.44*	—	0.79*	—	0.97*	—
EOS#	0.11#	—	0.21#	—	0.12#	—	0.83#	—
EOS%	0.16#	—	0.16#	—	0.23#	—	0.81#	—
BAS#	0.49#	—	0.14#	—	0.67#	—	0.32#	—
BAS%	0.66#	—	0.22#	—	0.54#	—	0.54#	—

Note: Pearson's correlation test (*) was used for normally distributed variables. Spearman's correlation test (#) was used for nonnormally distributed variables. “r” indicates the correlation coefficient (r > 0: positive correlation; r < 0: negative correlation; r = 0: no correlation). Bold values indicate statistically significant associations with altitude (p < 0.05).

3.4. Regression Analysis and Hemoglobin (Hb) Estimation Models

Considering the clinical relevance of hemoglobin variation across different altitudes in emergency department populations, this analysis aimed to explore the associations between altitude and hemoglobin levels and to construct an exploratory regression‐based model to characterize these relationships within the study cohort. In univariate linear regression analyses, altitude, sex (male), smoking status, and age were statistically associated with hemoglobin levels (p < 0.001). In addition, chronic hypoxic pulmonary disease, chronic kidney disease, malignancy, pregnancy, and other chronic diseases showed significant associations with hemoglobin levels (p < 0.001). Season was not significantly associated with hemoglobin levels (p > 0.05) (Table 4).

TABLE 4.

Univariable and multivariable linear regression analyses for estimating hemoglobin levels.

(a) Univariable linear regression of independent variables estimating hemoglobin level
Independent variable	r	R 2	Coefficient				t	p	Cook's distance (max)
			B	SE	Lower 95% CI	Upper 95% CI
Altitude	0.66	0.44	0.002	0.001	0.002	0.002	35.3	< 0.001	0.01
Age	−0.18	0.03	−0.02	0.003	—0.02	—0.01	−7.3	< 0.001	0.009
Sex (male)	0.55	0.31	2.14	0.55	1.98	2.30	26.5	< 0.001	0.013
Smoking (yes)	0.34	0.11	1.57	0.11	1.35	1.78	14.2	< 0.001	0.011
Season (winter)	0.02	0.001	0.06	0.02	−0.13	0.24	0.6	0.54	0.006
Pregnancy (yes)	0.09	0.01	−0.51	0.14	−0.79	−0.22	−3.5	< 0.001	0.009
Malignancy (yes)	0.11	0.01	−0.99	0.11	−1.44	−0.55	−4.4	< 0.001	0.055
Chronic hypoxic pulmonary disease (yes)	0.16	0.03	0.87	0,16	0.61	1.13	6.6	< 0.001	0.016
Chronic kidney disease (yes)	0.36	0.13	−2.89	0.19	−3.26	−2.52	−15.3	< 0.001	0.027
Other chronic diseases (yes)	0.33	0.1	−1.29	0.10	−1.48	−1.10	−13.6	< 0.001	0.009

(b) Comparison of multivariable linear regression models for hemoglobin level estimation
Regression model	R 2	Adjusted R 2	p	Number of variables	Log‐likelihood	AIC	BIC	Residual SE	VIF		10‐fold cross validation
									Min	Max	RMSE	R 2	MAE
1	0.439	0.439	< 0.001	1	−2802	5610	5627	1.41	1.0	1.0	1.41	0.441	1.12
2	0.686	0.686	< 0.001	3	−2340	4690	4717	1.05	1.04	1.12	1.05	0.687	0.80
3	0.723	0.722	< 0.001	4	−2240	4493	4525	0.99	1.05	1.23	0.99	0.725	0.75
4	0.824	0.823	< 0.001	7	−1880	3778	3826	0.79	1.14	1.29	0.79	0.824	0.62
5	0.832	0.831	< 0.001	9	−1845	3712	3771	0.77	1.04	1.34	0.78	0.829	0.60

(c) Model 5: Multivariable linear regression for hemoglobin level estimation
Independent variable	Coefficient					β	t	p
	B	SE	Lower 95% CI	Upper 95% CI	Rounded B
Constant	11.825	0.74	11 684	11 976	11.8	—	159.0	< 0.001
Altitude (m)	0.00196	0.0001	0.0019	0.002	0.002	0.64	56.7	< 0.001
Age (years)	−0.0265	0.001	−0.0289	−0.0241	−0.03	−0.26	−22.0	< 0.001
Sex (male)	1176	0.046	1086	1266	1.2	0.3	25.5	< 0.001
Smoking (yes)	0.839	0.0519	0.737	0.941	0.8	0.18	16.2	< 0.001
Chronic hypoxic pulmonary disease (yes)	0.699	0.0591	0.583	0.815	0.7	0.13	11.8	< 0.001
Chronic kidney disease (yes)	−1519	0.0899	−1696	−1342	−1.5	−0.19	−16.9	< 0.001
Other chronic diseases (yes)	−0.629	0.047	−0.721	−0.537	−0.6	−0.16	−13.4	< 0.001
Pregnancy (yes)	0.527	0.0663	0.397	0.657	0.5	0.09	7.9	< 0.001
Malignancy (yes)	−0.227	0.0964	−0.416	−0.038	−0.2	−0.03	−2.4	0.02

Note: (a) Univariable linear regression results are shown for each independent variable separately, with Pearson's correlation coefficient (r), the univariate R ² (coefficient of determination), unstandardized coefficients (B), standard errors (SE), 95% confidence intervals (CI), test statistics, and Cook's distance to assess influence of outliers. (b) Multivariable model comparisons present five models of increasing complexity. R ², adjusted R ², log‐likelihood, AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), residual standard error (SE), VIF (variance inflation factor), and 10‐fold cross‐validation metrics (RMSE: root mean square error; R ²; MAE: mean absolute error) are reported to evaluate model performance and generalizability. (c) Model 5 (final model) includes all selected predictors in a multivariable regression. Both unstandardized (B) and standardized (β) coefficients are provided along with corresponding confidence intervals and significance values. Rounded B values represent a simplified approximation of the model coefficients for practical use. Model 1: (altitude); Model 2: (altitude + age + sex); Model 3: (altitude+age + sex+smoking); Model 4: (altitude + age + sex + smoking + chronic hypoxic pulmonary disease + chronic kidney disease + other chronic diseases); Model 5: (altitude + age + sex + smoking + chronic hypoxic pulmonary disease + chronic kidney disease + other chronic diseases + pregnancy + malignancy).

Variables showing significant associations in univariate analyses were entered into multivariable regression models to explore their combined relationship with hemoglobin levels. Five models of increasing complexity were constructed and compared in terms of internal model fit within the study cohort. As additional variables were included, the proportion of explained variance (R ²) increased (Model 1 R ² = 0.44; Model 5 R ² = 0.832), while residual error metrics decreased (Model 5 RMSE = 0.78; MAE = 0.60). Variance inflation factors (VIFs) ranged from 1.04 to 1.34, indicating no relevant multicollinearity. Ten‐fold cross‐validation was performed to assess internal model stability across the candidate models, with Model 5 demonstrating the most favorable internal fit in this dataset. In addition, Akaike information criterion (AIC) and Bayesian information criterion (BIC) values decreased with increasing model complexity, further supporting improved model fit. Based on these internal comparisons, Model 5 was retained as the final exploratory multivariable model for presentation of the associations observed in this cohort. Model 5 included altitude, age, sex, smoking status, chronic hypoxic pulmonary disease, chronic kidney disease, other chronic diseases, pregnancy, and malignancy (Table 4).

3.5. Simplified Representation of the Final Exploratory Regression Model

To enhance interpretability, the coefficients of the final exploratory multivariable regression model were rounded to the nearest integers, allowing a simplified representation of the modeled associations between hemoglobin levels and the included variables (see Table 4; “Rounded B” column). Accordingly, the final model can be expressed in the following simplified form:

$$\begin{array}{cc}\mathbf{\text{Estimated}}\mathbf{H}\mathbf{b}\left(\mathbf{g}\mathbf{/}\mathbf{d}\mathbf{L}\right)\mathbf{=}\hfill & 11.8\mathbf{+}\left[0.002\times \text{Altitude}\left(\mathrm{m}\right)\right]\hfill \\ & \mathit{–}\left[0.03\times \mathrm{Age}\left(\text{year}\right)\right]\mathbf{+}\left[1.2\times \mathrm{Sex}\right]\hfill \\ & \mathbf{+}\left[0.8\times \text{Smoking}\right]\hfill \\ & \mathbf{+}\left[0.7\times \text{Chronic Hypoxic Pulmonary Disease}\right]\hfill \\ & \mathit{–}\left[1.5\times \text{Chronic Kidney Disease}\right]\hfill \\ & \mathit{–}\left[0.6\times \text{Other Chronic Diseases}\right]\hfill \\ & \mathbf{+}\left[0.5\times \text{Pregnancy}\right]\mathit{–}\left[0.2\times \text{Malignancy}\right]\mathbf{\pm }1.5\hfill \end{array}$$

Binary variables were coded as 1 (yes/present/male) and 0 (no/absent/female). In this study, the variable “chronic hypoxic pulmonary disease” was operationally defined as chronic respiratory conditions associated with sustained hypoxia, including COPD, asthma, and chronic bronchitis, as listed in Table 1. Other pulmonary conditions were classified under this category when they met the same criterion. All remaining chronic comorbidities, apart from chronic kidney disease, were grouped as “other chronic diseases” in the regression models.

4. Discussion

Acclimatization in response to hypobaric hypoxia is a physiological process that influences hematological, biochemical, and cardiorespiratory parameters at different altitudes. In this study, we examined altitude‐related variation in laboratory findings among emergency department patients residing between 0 and 2500 m. Blood tests play an important role in the evaluation of conditions such as bleeding, dehydration, renal dysfunction, and metabolic imbalance. However, laboratory reference intervals are generally based on uniform standards, and potential regional or altitude‐related variability is not always explicitly considered. Our findings suggest that altitude may be associated with measurable differences in several laboratory parameters, indicating that altitude could be an important contextual factor when interpreting results in both pediatric and adult populations.

We conducted this study at the emergency department of Harakani State Hospital in Kars, Turkey. Located at 1768 m in altitude, this center serves many patients living at different altitudes ranging from 0 to 2500 m, providing a suitable and varied cohort for evaluating low‐ and moderate‐altitude effects on laboratory parameters.

Nevertheless, the present findings should be interpreted within the context of an emergency department cohort rather than a healthy physiological population. Because laboratory measurements may be influenced by acute illness, inflammation, dehydration, medication use, and underlying comorbidities, the observed associations cannot be attributed exclusively to altitude exposure. Furthermore, some laboratory abnormalities discussed below may reflect the combined influence of altitude‐related physiological adaptation and concurrent disease‐related processes, including infection and inflammation.

Studies examining the influence of season and temperature on blood oxygenation have reported inconsistent findings. Pavone et al. [17] observed lower oxygen saturation in pediatric patients during winter months, whereas Qiu et al. [18] reported decreases in oxygen saturation with increasing temperature. In our cohort, SaO₂ and pO₂ levels were also lower in summer than in winter. This seasonal difference may reflect variability in physiological demand and environmental conditions, although further studies are needed to clarify the underlying mechanisms.

We found that the most frequently obtained laboratory tests in emergency department patients were glucose, creatinine, C‐reactive protein (CRP), and complete blood count parameters. These findings are consistent with previous reports from emergency care settings [19, 20]. The high frequency of these tests likely reflects their routine role in the initial evaluation of a broad range of acute presentations.

Under hypobaric hypoxia at higher altitudes, erythrocyte‐related adaptations may occur, including changes in blood viscosity and erythrocyte indices. Numerous studies have reported increases in red blood cell parameters such as hematocrit, MCV, MCH, MCHC, RDW, and hemoglobin with increasing altitude [12, 13, 14, 15, 21, 22, 23, 24, 25, 26, 27, 28]. Consistent with literature, we observed positive associations between altitude and hemoglobin‐related indices in adults and in the 13–17‐year pediatric subgroup. These findings suggest that altitude may represent an important contextual factor when interpreting hemoglobin and erythrocyte indices, particularly in populations residing at low‐to‐moderate altitudes.

Hemoglobin demonstrated one of the strongest altitude‐associated patterns among the laboratory parameters evaluated in this cohort. To further characterize this relationship, we applied an exploratory multivariable regression framework incorporating altitude, demographic factors, and selected comorbid conditions. This approach was intended to provide a structured description of the factors associated with hemoglobin variation within the study population, rather than to propose a definitive clinical prediction tool. The simplified representation of the final model offers an interpretable summary of these associations and may serve as a basis for future validation studies in independent cohorts.

To better understand the factors associated with hemoglobin levels in this cohort, we examined the contributions of key demographic and clinical variables within the multivariable regression framework. Consistent with prior literature, altitude showed a positive association with hemoglobin levels, while age demonstrated a modest negative association [1, 2, 3, 4, 5, 9, 12, 13, 14, 15, 26]. Sex and smoking status were also associated with higher hemoglobin values, in line with expected physiological patterns [15, 29, 30, 31]. Pregnancy showed differing directions in univariate and multivariable analyses. While the unadjusted association was consistent with the well‐recognized physiological hemodilution of pregnancy, the direction of the coefficient changed after adjustment for altitude, age, smoking status, malignancy, and chronic diseases. This finding likely reflects the influence of confounding factors and emphasizes that pregnancy‐related changes in hemoglobin should be interpreted within the broader demographic and clinical context rather than as an isolated effect. The observed complexity of pregnancy‐related hematological variation also suggests that more detailed evaluations, including trimester‐specific analyses, may be warranted in future studies [15, 32, 33].

Malignancy and chronic organ dysfunction are well‐recognized contributors to hemoglobin variation. In our cohort, malignancy was associated with slightly lower hemoglobin values, consistent with reports that anemia is common in patients with cancer [34, 35, 36]. Similarly, chronic kidney disease showed a negative association with hemoglobin levels, in line with reduced erythropoietin production observed in CKD [37, 38]. Chronic hypoxic pulmonary diseases, such as COPD, were associated with higher hemoglobin values, reflecting secondary erythrocytosis described in previous studies [39, 40, 41, 42]. Other chronic comorbidities were grouped separately and demonstrated modest negative associations with hemoglobin, potentially reflecting multifactorial mechanisms of chronic disease‐related anemia, including inflammation, erythropoietin suppression, fluid‐electrolyte imbalance, and nutritional or absorption disturbances [43, 44, 45, 46, 47]. These findings further emphasize the importance of considering underlying comorbidity patterns when interpreting hemoglobin levels in heterogeneous emergency department populations.

In pediatric patients, we observed fewer statistically significant altitude‐associated correlations compared with adults, particularly in children aged ≤ 12 years. Prior studies have reported mixed findings regarding the magnitude of altitude‐related laboratory variation in pediatric populations [8, 48]. These differences may reflect variation in study altitude ranges, cohort characteristics, or developmental factors. Given the smaller pediatric subgroup sizes and the exploratory nature of these analyses, these observations should be interpreted cautiously and primarily as hypothesis‐generating. Further multicenter studies with larger pediatric cohorts are needed to clarify age‐specific patterns of adaptation to altitude.

Sevik et al. [49] reported in their meta‐analysis that pO₂ decreases with altitude, which is associated with a lower oxygen concentration. This decrease reduces O₂Hb and increases HHb; hyperventilation and transient alkalosis occur; however, renal compensation balances pH and pCO₂ [13, 25, 50, 51, 52, 53]. Similarly, we detected a negative correlation between pO₂ and SaO₂ with altitude in both children and adults. However, HCO₃ and base excess were negatively correlated with altitude only in adults. Although this situation suggests that compensatory mechanisms such as ventilation adjustment and renal bicarbonate regulation may contribute to maintaining overall acid–base balance within the altitude range studied, the reliability of the cohort consisting of emergency department patients in this regard should also be questioned. In addition, oxygenation‐related parameters were available only in patients who underwent arterial blood gas analysis as part of routine clinical evaluation. Therefore, the relatively low mean SaO₂ values observed in this cohort may partly reflect indication‐related selection of patients with respiratory symptoms, suspected hypoxemia, or other conditions prompting blood gas assessment, rather than altitude exposure alone.

Previous studies have reported mixed explanations for altitude‐associated increases in carboxyhemoglobin (COHb), including both environmental exposures and endogenous physiological mechanisms related to hemoglobin turnover at higher elevations [54, 55, 56]. In our cohort, COHb levels also showed a positive association with altitude. However, this association should be interpreted cautiously, as environmental exposures and disease‐related factors not captured in the present dataset may also influence COHb levels.

Hypoxia decreases methemoglobin reductase enzyme activity at high altitudes and increases blood MetHb levels [57]. Similarly, we observed a positive association between MetHb and altitude in adults. Nevertheless, other clinical factors associated with acute illness may also contribute to MetHb variation and should be considered when interpreting this finding.

In the literature, it has been reported that fluid retention increases at high altitudes, the GFR decreases and creatinine increases; additionally, hyperuricemia occurs due to excessive erythrocytosis [1, 2, 12, 13, 14, 58, 59]. Altitude was associated with higher creatinine and lower GFR values in adults and in the 13–17‐year subgroup, whereas a positive association between urea and altitude was observed only among adults.

Serum calcium levels showed a positive association with altitude in adults, consistent with some prior observations in high‐altitude populations [60, 61]. The clinical significance and underlying mechanisms of this finding remain uncertain and warrant further investigation in more controlled settings.

In line with prior reports suggesting that hypoxic exposure may affect liver‐related biomarkers, our study found that ALT and AST levels showed a positive correlation with altitude in adults [62]. Nevertheless, these findings should be interpreted with caution, as acute inflammatory conditions, infections, medication exposure, and other disease‐related factors common in emergency department populations may also contribute to variations in liver‐related biomarkers.

Previous studies have proposed several mechanisms for altitude‐associated increases in bilirubin levels, including altered hepatic conjugation, increased hemolysis related to polycythemia, and adaptive antioxidant responses under hypoxic stress [63, 64, 65]. Consistent with these reports, both direct and indirect bilirubin levels showed positive associations with altitude in adults in our cohort. However, because bilirubin levels may also be influenced by acute illness, inflammatory states, and underlying hepatic or systemic disorders, a direct causal relationship with altitude cannot be established from the present data.

Altitude‐related hypoxia has been associated with endothelial stress and hematologic adaptation, which may influence platelet indices during prolonged exposure [66, 67, 68, 69]. In our study, platelet count (PLT) and plateletcrit (PCT) were negatively associated with altitude in both adults and children, whereas mean platelet volume (MPV) and platelet distribution width (PDW) showed positive associations with altitude only in adults.

Previous studies in healthy high‐altitude populations have reported variable effects of hypoxia on leukocyte indices, with some suggesting altered immune cell distributions and potentially higher reference expectations [26, 27]. In our cohort, however, neither total white blood cell count nor its subtypes showed consistent significant associations with altitude. This discrepancy may reflect the heterogeneous emergency department population and the influence of acute illness, underscoring the need for further studies in more controlled settings.

Overall, this study provides a comprehensive exploratory assessment of altitude‐associated variation in routinely obtained laboratory parameters across pediatric and adult emergency department populations residing between 0 and 2500 m. Our findings indicate that several hematologic, metabolic, and oxygenation‐related measures show measurable associations with residential altitude, suggesting that altitude may represent an important contextual factor when interpreting laboratory results in heterogeneous clinical settings. However, because the study population consisted of emergency department patients rather than healthy individuals, the observed relationships likely reflect the combined influence of altitude exposure, acute illness, underlying comorbidities, and other clinical factors. Accordingly, these findings should be interpreted as exploratory observations rather than evidence of direct causal physiological effects of altitude. Further multicenter investigations in healthier reference cohorts and independent populations are needed to confirm these patterns and to clarify their potential implications for clinical interpretation at low‐to‐moderate altitudes.

4.1. Limitations

This study has several important limitations. It was conducted at a single center within a residential altitude range of 0–2500 m and did not account for broader ethnic or genetic diversity. The cohort consisted exclusively of emergency department patients, representing a clinically heterogeneous population in whom acute illness, inflammation, dehydration, medication use, or metabolic disturbances may influence laboratory parameters, limiting generalizability to healthy reference populations. Consequently, some observed altitude‐associated relationships may partially reflect underlying disease processes rather than physiological adaptation to altitude alone. The pediatric sample size was smaller than the adult sample size, and infants aged 0–1 years were excluded due to low case numbers and potential maternal blood effects; therefore, pediatric findings require further confirmation. Pregnancy was recorded only as present or absent without trimester stratification, and smoking status and chronic diseases were similarly captured without information on severity, duration, or treatment. Although residence at the recorded address for at least 2 weeks prior to admission was confirmed, longer‐term altitude exposure history was unavailable; therefore, some degree of exposure misclassification cannot be excluded. In addition, the regression findings should be interpreted as exploratory, as the model was developed within a single cohort and has not been externally validated in independent populations. Furthermore, a large number of laboratory parameters were evaluated across multiple subgroup analyses, increasing the possibility of false‐positive findings due to multiple testing. Although the principal associations demonstrated consistent statistical support and biological plausibility, some weaker associations should be interpreted with caution. Therefore, the present findings should be considered hypothesis‐generating and require confirmation in larger, independent, and preferably multicenter cohorts. Furthermore, arterial blood gas measurements were obtained according to clinical indication rather than systematically in all emergency department patients. Consequently, oxygenation‐related parameters may have been influenced by selection bias, as patients undergoing blood gas analysis were more likely to have respiratory or metabolic conditions prompting such testing.

5. Conclusion

This study provides an exploratory assessment of altitude‐associated variation in routinely obtained laboratory parameters among pediatric and adult emergency department patients residing between 0 and 2500 m. Several hematologic, metabolic, and oxygenation‐related measures demonstrated measurable associations with residential altitude, suggesting that altitude may represent an important contextual factor when interpreting laboratory results in low‐to‐moderate altitude settings. The multivariable regression model presented offers an interpretable summary of factors associated with hemoglobin variation within this cohort; however, its findings should be regarded as hypothesis‐generating and require external validation in independent and healthy reference populations. Future multicenter studies are warranted to confirm these patterns and to better define the role of altitude in clinical laboratory interpretation.

Author Contributions

A.Y.: corresponding author; design and execution of the study; concept; visualization; data collection; data analysis; writing. M.Y.: design and execution of the study, research, observation, data collection, writing. The article was written with the participation of all the authors. All the authors contributed to the interpretation of the results and have read and approved the final version of the article.

Funding

The authors have nothing to report.

Ethics Statement

The study protocol was approved by the Kafkas University Faculty of Medicine Ethics Committee (date: 30.10.2024, no: 2024/08‐02). The Declaration of Helsinki was fully complied with, and the data required to protect patient privacy were obtained from clinical records without any clinical intervention.

Consent

As this was a retrospective study, the ethics committee waived the need for informed consent.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.