Abstract
Background
The prevalence of obesity is rapidly increasing, presenting a significant risk for obesity-related glomerulopathy. Thus, it is crucial to prioritize early detection of obesity related glomerulopathy. Biomarkers, such as NGAL, TIMP-2, and IGFBP-7 have shown promise in detecting subclinical kidney injury. The study aimed to explore whether obese individuals exhibit higher levels of NGAL, TIMP-2 and IGFB7 as compared to non-obese individuals.
Methods
A total of 160 adults without any comorbidities, participated in this cross-sectional study. Participants were categorized into four BMI-based groups: Group A (normal weight, BMI 18.0–24.9 kg/m²), Group B (overweight, BMI 25.0–34.9 kg/m²), Group C (obese, BMI 35.0–39.9 kg/m²), and Group D (morbidly obese, BMI ≥ 40.0 kg/m²). NGAL was measured from serum samples, while TIMP2 and IGFBP7 were extracted from urine samples. All biomarkers were assessed using the ELISA method. Kruskal-Wallis test and spearman correlation were performed to evaluate the association between these biomarkers and BMI-related factors.
Results
We observed a statistically significant difference in the median serum NGAL levels (p < 0.001). However, no significant differences were found in the median levels of TIMP-2 or IGFBP-7 across the BMI categories. We also found a positive association between serum NGAL and various parameters of adiposity. However, this correlation also remained insignificant for urine TIMP-2 and urine IGFBP-7.
Conclusions
Serum NGAL levels are significantly elevated in individuals with higher degrees of obesity and shows positive correlation with various measures of adiposity. Our findings highlight the need of early screening for renal stress in individuals with obesity.
Keywords: Obesity, Subclinical kidney injury, Obesity related glomerulopathy, NGAL, TIMP-2, IGFBP-7
Background
Obesity represents a significant global public health challenge, with prevalence rates projected to rise steadily across all populations by 2030 [1]. In recent years, the burden has grown more sharply in developing nations. According to the National Family Health Survey-5 (NFHS-5), one in every four Indians is currently living with obesity. Overweight and obesity affect both men and women across a wide range of socioeconomic groups particularly the elderly, individuals residing in urban areas and those from diverse economic backgrounds [2].
Obesity adversely affects multiple organs notably the heart, liver and kidneys. It is a key contributor to progressive renal dysfunction, termed obesity-related glomerulopathy (ORG), characterized by glomerulomegaly with or without focal segmental glomerulosclerosis (FSGS) [3]. Approximately 10% of individuals with ORG are estimated to progress to end-stage renal disease (ESRD) within 6.2-7 years of diagnosis [4]. The first case of ORG- was reported in 1974 [5]. A retrospective cohort study of nearly 1.2 million adolescents has also shown a significant association between individuals living with overweight and obesity and an increased risk of ESRD requiring treatment [6]. Furthermore, Hu et al. reported that the prevalence of ORG nearly doubled during 2014–2018 as compared to 2009–2013 [7]. The rate of increase varies across countries, and is influenced by economic, lifestyle, and environmental factors.
The pathophysiology of ORG involves three interconnected mechanisms: hemodynamic alterations, adipose tissue-derived factors and insulin resistance [8]. These pathways synergistically contribute to glomerular hyperfiltration, hyperperfusion, and glomerular hypertension, ultimately causing increased renal volume, cellular degeneration and structural damage [9]. Additionally, perivascular fat deposition and hyperlipidemia exacerbate hemodynamic stress, resulting in heightened glomerular filtration pressure and injury [10].
In current clinical practice, serum creatinine level and urine output remain the standard indicators of renal dysfunction however both have well-recognized limitations including poor sensitivity and delayed response to the injury [11]. Several novel biomarkers like urinary and serum neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitors of metalloproteinases (TIMP) and insulin-like growth factor binding protein − 7 (IGFBP-7) have been developed to detect renal injury at the early stages [12, 13].
Evidence suggests that 15%–20% of patients with elevated tubular injury markers-despite normal serum creatinine levels- have a two to threefold increased risk of mortality or the need for renal replacement therapy [14]. Furthermore, a meta-analysis in paediatric populations showed NGAL as a promising biomarker for the early detection of kidney injury. The diagnostic accuracy of urinary NGAL and serum NGAL was found to be 85.2% and 84.7% respectively [15]. It’s also observed that in individuals with moderate renal disease, the elevated serum NGAL level independently predicts its progression to chronic kidney disease (CKD) [16]. Similarly, urine TIMP-2 and IGFBP-7 have emerged as reliable markers for subclinical injury before a rise in serum creatinine becomes apparent [17].
Although the association between obesity and kidney disease is well-established, the lack of non-invasive diagnostic tools continues to hinder clinical translation. Conventional assessment often fails to accurately reflect true renal function in individuals living with overweight or obese [4]. Thus, serum NGAL, Urine TIMP-2 and IGFBP7 offers a mean to predict diagnosis of early renal damage in obese individuals.
Therefore, the present study was designed to investigate whether a relationship exists between early renal injury markers- serum NGAL, Urinary TIMP-2 and IGFBP-7 and the degree of Obesity. Additionally, we examined their associations with body composition parameters to understand adiposity associated renal stress better.
We hypothesized that the degree of obesity would be positively associated with levels of renal injury biomarkers and that biomarker levels would increase progressively with rising BMI indicating a positive association between adiposity and subclinical renal injury.
Methods
Study design
This observational study employed a cross-sectional design and a total of 160 participants were recruited from the Outpatient Department and the Obesity & Metabolic Clinic of the All India Institute of Medical Sciences (AIIMS), New Delhi, India from March 2024 to May 2025. The study was approved by the Institutional Ethics Committee of the All India Institute of Medical Sciences (AIIMS), New Delhi (Ref No: AIIMSA00780/02.02.2024, RP-35/2024).
Data collection
The study participants were recruited by trained research scientists currently working in the department of Medicine, AIIMS, New Delhi. Asian Indian adults, aged 18–55 years, were enrolled in the study after obtained their informed written consent and stratified into four groups based on Body Mass Index(BMI), using the obesity and non-obesity classification according to WHO classification: Group A: BMI 18.0–24.9 kg/m², Group B: BMI 25.0–34.9 kg/m², Group C: BMI 35.0–39.9 kg/m², and Group D: BMI ≥ 40.0 kg/m². Although WHO Asia-Pacific cut-offs are useful for risk stratification in Asian populations, WHO BMI categories were used in this study to allow assessment across obesity severity and to facilitate comparability with prior biomarker-based renal studies. Inclusion criteria included age (18–55 years) and willingness to provide informed consent. Participants with any of the following conditions were excluded: Type 1 or Type 2 diabetes mellitus, hypertension, acute or chronic kidney disease, active infection, use of nephrotoxic medications, pregnancy and lactation.
All enrolled underwent a comprehensive clinical evaluation including detailed history and physical examination along with blood pressure (BP) measurement. Anthropometric measurements recorded were weight, height and BMI. Body weight was measured using an electronic weighing scale and height was measured using a stadiometer.
Body composition was assessed using bioelectrical impedance analysis (BIA) with the (Accunic S720) body composition analyser. Participants stood barefoot on a metal sole-plate which incorporates the electrodes; hence segmental impedance was measured through the legs and lower trunk using five frequencies (5, 50, 100, 250 and 500 kHz) and eight electrodes for whole body testing. Appendicular skeletal muscle mass was calculated by summing the muscle masses of both arms and legs (in Kg). Appendicular skeletal muscle index (ASMI) was calculated by dividing the appendicular muscle mass by square of height (in meters) [18].
All biochemical tests were performed at the central laboratory, AIIMS New Delhi. Complete Blood Count was analysed using direct and fluorescent flow cytometry. Serum and urine creatinine were measured using Jaffe method. Plasma glucose was analysed using hexokinase enzymatic method. Glycated haemoglobin (HbA1c) analysis was done using the Turbidimetric Inhibition Immunoassay (TINIA) method. The CHOD-PAP (cholesterol oxidase–peroxidase) method was used to test total cholesterol. Serum triglycerides (TGL), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using standard enzymatic colorimetric assays . Urine albumin concentrations were measured using an immunoturbidimetric test. Serum uric acid was measured using enzymatic colorimetric method. The neutrophil–lymphocyte ratio (NLR) was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count obtained from the complete blood count analysis.
All serum samples were aliquoted and stored at -80 °C to estimate serum neutrophil gelatinase-associated lipocalin (NGAL). Spot urine samples (≥ 10 mL) were collected, centrifuged at 3000 rpm for five minutes, and the supernatants were stored at − 80 °C to analyse urinary TIMP-2 and IGFBP-7. Serum NGAL (after dilution), and urine TIMP-2, urine IGFBP-7 (undiluted) were measured using enzyme-linked immunosorbent Assay (ELISA) kits. The ELISA kits used were Human NGAL ELISA Kit (ImmunoTag, Catalogue No. ITLK01063), Human TIMP-2 ELISA Kit (Catalogue No. ITLK02312), and Human IGFBP-7 ELISA Kit (Catalogue No. ITLK03007), all from G-Biosciences, St. Louis, MO, USA. ELISA assays were performed according to the manufacturers’ protocols, and absorbance was measured at 450 nm in an ELISA reader (Multimode microplate reader, Synergy BioTek, USA). Serum NGAL concentrations were multiplied by the dilution factor of 200 to obtain final values.
Statistical analysis
The sample size was estimated assuming an effect size of 0.6 (mean difference of 90 and pooled standard deviation 160) [17], with a power of 80% and a significance level of 5%, yielding a required total of 45 participants in each arm. However, 160 participants were ultimately enrolled due to logistical limitations and recruitment feasibility within the study timeframe.
Data was analyzed using Stata 11.2 software (StataCorp LP, Texas, USA) and results were expressed in mean ± standard deviation for continuous data and median for non-continuous data. In case of categorical variable between the groups, differences were compared using Chi square test. We checked the data for normality using Shapiro-Wilk test. Upon finding that the data was not normally distributed, we compared the levels of NGAL, TIMP-2, IGFBP-7 among the BMI groups using Kruskal-Wallis test. On finding a statistically significant difference between the values among the groups, we further performed post hoc multiple comparisons using Mann-Whitney U test with Bonferroni correction (i.e. p = 0.0083). Correlation between continuous were analysed using spearman correlation. P-values less than 0.05 were considered statistically significant.
Results
Result of descriptive statistics are given in Table 1. Mean (± SD) age was 38.5 (9.3) years. 70% participants were female (n = 112). Mean BMI (± SD) was 22.4 (2.0) Kg/m2 in group A, 28.7 (2.7) Kg/m2 in group B, 37.2(1.4) Kg/m2 in group C, and 46.1 (5.4) Kg/m2 in group D (Table 2). The median (IQR) of neutrophil lymphocyte ratio in group A was 1.6 (1.3–2.4), followed by 2.1 (1.6–3.4), 2.1 (1.8–2.6), and 2.2 (1.8–2.8) in Group B, C, and D respectively.
Table 1.
Descriptive statistics of participants across groups of BMI
| Groups | p-value | |||||
|---|---|---|---|---|---|---|
| Total (N = 160) | A (n = 45) | B (n = 45) | C (n = 45) | D (n = 25) | ||
| Gender | 0.01 | |||||
| Male (%) | 48 (30) | 19 (42.2) | 18 (40.0) | 7 (15.6) | 4 (16) | |
| Female (%) | 112 (70) | 26 (57.8) | 27 (60.0) | 38 (84.4) | 21 (84) | |
| Age | 38.5 (9.3) | 33.8 (9.4) | 40.2 (10.2) | 41.2 (7.2) | 39.2 (8.1) | 0.12 |
| Weight (Kg) | 78.9(21.2) | 57.2(8.0) | 72.0(11.3) | 91.2(9.5) | 108.4(17.5) | < 0.001 |
| Height (cm) | 157.3(9.0) | 159.6(9.7) | 158.1(9.1) | 156.5(8.3) | 153.2(7.9) | 0.60 |
| BMI (Kg/m2) | 32.0 (8.7) | 22.4 (2.0) | 28.7 (2.7) | 37.2 (1.4) | 46.1 (5.4) | < 0.001 |
| PBF(%) | 41.9 (11.2) | 30.3 (8.4) | 38.6 (7.0) | 49.1 (5.5) | 54.2 (5.8) | 0.03 |
| Body Fat Mass (Kg) | 34.5 (16.2) | 16.9 (4.6) | 27.2 (6.1) | 44.4 (4.3) | 58.6 (11.9) | < 0.001 |
| Visceral Fat Mass (Kg) | 5.5 (2.8–8.6) | 2.1 (1.6–2.1) | 3.8 (3.4–5.2) | 7.8 (7.2–8.8) | 10.9 (9.9–13.3) | < 0.001 |
| Visceral fat area (cm2) | 210 (108–307) | 83 (60–109) | 151.5 (111.5-218.5) | 286 (256–330) | 387(347–447) | < 0.001 |
| ASMI (Kg/m2) | 6.9 (1.4) | 5.9 (1.0) | 6.6 (1.1) | 7.5 (1.6) | 8.0 (1.2) | 0.01 |
| Fat Free Mass (Kg) | 44.9 (9.8) | 40.1 (8.6) | 44.5 (8.9) | 46.8 (9.4) | 50.0 (10.5) | 0.70 |
| Fasting glucose (mg/dL) | 91.9 (9.1) | 91.8 (9.3) | 90.8 (8.1) | 93.2 (10.7) | 91.5 (7.6) | 0.17 |
| HbA1c (%) | 5.5 (0.5) | 5.3 (0.4) | 5.4 (0.5) | 5.7 (0.4) | 5.8 (0.3) | 0.42 |
| Total Cholesterol (mg/dL) | 175.8 (37.3) | 171. 9 (40.6) | 174.8 (35.0) | 180.3 (33.7) | 176.8 (42.5) | 0.45 |
| High Density Lipoprotein (mg/dL) | 46.0 (10.9) | 48.0 (11.4) | 44.3 (11.1) | 46.4 (11.1) | 45.2 (8.6) | 0.44 |
| Low Density Lipoprotein (mg/dL) | 102.5 (29.9) | 97.8 (32.4) | 101.7 (31.0) | 107.2 (28.5) | 104.3 (25.7) | 0.59 |
| Triglyceride (mg/dL) | 125.9 (54.0) | 120.6 (55.0) | 129.7 (49.3) | 129.5 (57.7) | 122.3 (55.6) | 0.77 |
| Haemoglobin (g/dL) | 12.8(1.7) | 13.3(1.8) | 12.8(1.9) | 12.4(1.3) | 12.4(1.7) | 0.08 |
| Total Leukocyte Count (103/µL) | 7.8(2.4) | 6.8(1.6) | 7.6(1.8) | 8.3(3.2) | 9.0(2.0) | < 0.001 |
| Neutrophil (103/µL) | 4.7 (3.4–5.9) | 3.4 (2.8–4.9) | 4.8 (3.6–5.7) | 4.7 (3.5–6.2) | 5.8 (4.7–6.3) | < 0.001 |
| Lymphocyte (103/µL) | 2.1 (1.7–2.7) | 2.0 (1.7–2.6) | 2.0 (1.6–2.6) | 2.0 (1.7–2.7) | 2.5 (1.8–3.1) | 0.24 |
| Neutrophil/Lymphocyte Ratio | 2.0 (1.6–2.9) | 1.6 (1.3–2.4) | 2.1 (1.6–3.4) | 2.1 (1.8–2.6) | 2.2 (1.8–2.8) | 0.03 |
| Platelet Count (103/µL) | 219.6 (83.9) | 199.7 (65.8) | 186.6 (60.9) | 242.1 (98.8) | 274.2 (84.9) | 0.01 |
| Serum Uric Acid (mg/dL) | 5.0 (1.4) | 4.6 (1.3) | 5.1 (1.5) | 5.1 (1.5) | 5.1 (1.4) | 0.62 |
| Serum Creatinine (mg/dL) | 0.70 (0.2) | 0.70 (0.2) | 0.74(0.1) | 0.68(0.2) | 0.66(0.2) | 0.84 |
| Urine Albumin (mg/L) | 6.2 (2.1–13.3) | 4.2 (1.8–10.1) | 4.9 (1.9–11.9) | 7.6 (2.6–15.6) | 11.3 (5.5–25.2) | 0.02 |
| Urine creatinine (mg/dL) | 95.4 (47.5-151.9) | 79.8 (42.3-155.2) | 92.2 (38.8-176.8) | 90.9 (57.2-120.2) | 122.9 (93.3-155.8) | 0.26 |
| Urine ACR (mg/g) | 5.9 (3.6–12.2) | 4.5 (3.1–7.2) | 6.8 (3.0-13.4) | 7.1 (3.7–17.2) | 6.6 (5.5–14) | 0.10 |
| eGFR (ml/min/1.73 m2) | 115 (107–124) | 121 (111–128) | 111 (95-120.5) | 113 (105–120) | 114 (110–126) | 0.02 |
Qualitative data reported as n (%)Quantitative data reported as Median (IQR) or Mean ± SD, as appropriate
Group A: BMI 18.5–24.9 Kg/m2
Group B: BMI 25-34.9- Kg/m2
Group C: BMI 35-39.9 Kg/m2
Group D: BMI ≥ 40 Kg/m2
Table 2.
Association between novel biomarkers of injury (NGAL, TIMP-2, IGFBP-7) and degree of obesity
| Groups | Overall p-value |
p- AB |
p- AC |
p- AD |
p- BC |
p- BD |
p- CD |
|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total (N = 160) | A (n = 45) | B (n = 45) | C (n = 45) | D (n = 25) | ||||||||
| NGAL (ng/mL) | 595. 2 (317.7-1178.3) | 495.2 (252-766.4) | 342.2 (221.6-575.6) | 1006 (439-1388.6) | 890.8 (730.8-1346.6) | < 0.001 | 0.134 | 0.002 | 0.001 | < 0.0001 | < 0.0001 | 0.704 |
| TIMP 2 (ng/mL) | 0.83 (0.66–1.78) | 0.83 (0.69–1.60) | 0.79 (0.66–2.35) | 0.95 (0.65–1.56) | 0.96 (0.59-6.00) | 0.10 | - | - | - | - | - | - |
| IGFBP7 (ng/mL) | 0.10 (0.00-0.22) | 0.10 (0.04–0.23) | 0.10 (0-0.25) | 0.10 (0-0.20) | 0.07 (0-0.14) | 0.51 | - | - | - | - | - | - |
The median NGAL (ng/mL) concentrations, along with their respective interquartile range, across the four groups were: 495.2 (252-766.4) in Group A, 342.2 (221.6-575.6) in Group B, 1006 (439-1388.6) in Group C, and 890.8 (730.8-1346.6) in Group D (Fig. 1). Median values of TIMP-2 (ng/mL) were 0.83 (0.69–1.60), followed by 0.79 (0.66–2.35), 0.95 (0.65–1.56), and 0.96 (0.59-6.00) across Groups B, C and D groups respectively. Similarly, IGFBP-7 (ng/mL) showed medians of 0.10 (0.04–0.23) in Group A, 0.10 (0.00-0.25) in Group B, 0.10 (0.00-0.20) in Group C, and 0.07 (0.00-0.14) in Group D (Table 2) (Fig. 2).
Fig. 2.
Median urine TIMP-2(ng/mL) and IGFBP-7 (ng/mL) levels across BMI groups
Fig. 1.
Median serum NGAL (ng/mL) levels across BMI groups
Upon comparing the values of the three biomarkers across the four BMI groups, we observed a statistically significant difference in the median serum NGAL levels (p < 0.001). However, no significant differences were found in the median levels of TIMP-2 or IGFBP-7 across the BMI categories (Table 1).
Post hoc multiple comparisons showed that NGAL levels were significantly higher in the obese groups—Group C (p = 0.002) and Group D (p = 0.001)—compared to the lean Group A. Likewise, both Group C and Group D had significantly elevated NGAL levels compared to Group B (p < 0.0001 for both comparisons) (Table 2).
Spearman correlation analysis was performed to evaluate the association between serum NGAL levels and body composition parameters. NGAL levels showed a moderate statistically significant positive correlation with BMI (ρ = 0.35, p < 0.001), percentage body fat (PBF) (ρ = 0.33, p < 0.001), fat mass (kg) (ρ = 0.34, p = < 0.001), visceral fat mass (kg) (ρ = 0.35, p < 0.001) and visceral fat area (cm²) (ρ = 0.36, p < 0.001), indicating a consistent association with adiposity. In contrast, the correlations between NGAL and appendicular skeletal muscle index (ASMI) (ρ = 0.12, p = 0.14) as well as fat-free mass (ρ = 0.05, p = 0.51) were not statistically significant (Table 3).
Table 3.
Correlation between novel biomarkers of kidney injury (NGAL, TIMP-2, IGFBP-7) with BMI and body composition parameters
| NGAL (ng/mL) | TIMP-2 (ng/mL) | IGFBP-7 (ng/mL) | ||||
|---|---|---|---|---|---|---|
| Spearman’s Rho (ρ) | p-value | Spearman’s Rho (ρ) | p-value | Spearman’s Rho (ρ) | p-value | |
| BMI (Kg/m2) | 0.35 | < 0.001 | -0.06 | 0.43 | -0.15 | 0.06 |
| Percentage body fat (%) | 0.33 | < 0.001 | -0.08 | 0.31 | -0.10 | 0.24 |
| Fat mass (Kg) | 0.34 | < 0.001 | -0.07 | 0.36 | -0.16 | 0.05 |
| Visceral fat mass (Kg) | 0.35 | < 0.001 | -0.05 | 0.55 | -0.15 | 0.06 |
| Visceral fat area (Kg) | 0.36 | < 0.001 | -0.04 | 0.60 | -0.11 | 0.16 |
| ASMI (Kg/m2) | 0.12 | 0.14 | 0.02 | 0.81 | -0.16 | 0.05 |
| Fat free mass (Kg) | 0.05 | 0.51 | 0.06 | 0.45 | -0.08 | 0.30 |
However, in contrast to serum NGAL, urine TIMP-2 did not show a statistically significant association with any of the assessed body composition parameters including BMI (p = 0.43), percentage body fat (p = 0.31), fat mass (p = 0.36), visceral fat mass (p = 0.55), visceral fat area (p = 0.60), appendicular skeletal muscle index (p = 0.81), or fat-free mass (p = 0.45). Similarly, IGFBP7 levels were not significantly associated with body composition parameters (Table 3).
Discussion
In our current study, we evaluated the association of the serum and urine biomarkers of early kidney damage (Serum NGAL, urine TIMP-2, and urine IGFBP-7) with a degree of obesity. Our findings demonstrate that the levels of serum NGAL are significantly raised in groups with higher BMI as compared to lower BMI groups. Moreover, we found a significant positive correlation between BMI and serum NGAL levels. These results are in line with evidence from existing literature on the link between obesity and NGAL levels. Eguchi et al. (2019) reported elevated urine NGAL levels among overweight/obese patients as compared to those with normal weight. However, their findings were based on a different study population as they exclusively included participants with early-stage chronic kidney disease [19]. Another study by Wang et al. [20] involving adults aged 33–72 years, irrespective of their metabolic syndrome status, showed a significant increase in serum NGAL among individuals with obesity in comparison to the non-obese. In contrast, our study specifically examined this association between obese populations without diabetes. Despite a restrictive clinical profile of our study population, a similar link between BMI and serum NGAL suggests that obesity alone may contribute to kidney damage, independent of metabolic dysfunction [20].
Our results demonstrated that there are no variations in urine TIMP-2 levels across BMI groups. This is supported by another study among Tunisian adults which also highlighted that TIMP-2 is not associated with BMI. However, in contrast to our study, the aforementioned study pertained to plasma TIMP-2 and included participants with metabolic syndrome as well. We also did not find a significant difference in urine IGFBP-7 levels across the four groups of BMI. Although urine IGFBP-7 is a validated biomarker of renal injury [21], to our knowledge, no previous study has examined the association between urine IGFBP-7 and obesity. The absence of a statistically significant difference in TIMP-2 and IGFBP-7 levels between obese and non-obese groups need to interpret carefully. One possible explanation is the relatively preserved renal function of participants included in the our study, as these biomarkers are known to reflect early tubular cell cycle arrest and are more prominently elevated in states of overt or evolving kidney stress. Additionally, obesity represents a heterogeneous metabolic condition; the inclusion of individuals with metabolically healthy obesity may have diluted biomarker differences attributable to adiposity alone. The cross-sectional nature of the study may further limit the ability to capture early subclinical tubular injury, which may precede measurable changes in circulating biomarkers.
Furthermore, the results of this study underscore a positive association between serum NGAL and various parameters of adiposity, including body fat percentage, fat mass, visceral fat mass, and visceral fat area. However, this correlation also remained insignificant for urine TIMP-2 and urine IGFBP-7. Another study by Xu et al. (2018) also showed a significant association between visceral fat area and circulating NGAL in a cohort of middle-aged and elderly individuals [22]. Wang et al. reported similar results on link between serum NGAL and body fat percentage among Chinese adults [20]. Impaired adipose tissue expandability and an elevated susceptibility of visceral adipose tissue to lipolysis may result in overflow of non-esterified fatty acids (NEFA) into the bloodstream, which could cause its deposition in non-adipose organs including kidney leading to fatty kidney. These processes could account for the elevated NGAL observed in patients with greater visceral adiposity levels [23].
We also found a significant association between neutrophil/lymphocyte ratio (NLR) and BMI groups in our study. The association between NLR and BMI reflects obesity-related chronic low-grade inflammation, characterised by neutrophilia, relative lymphocyte suppression, and increased thrombopoiesis, which may also explain higher leukocyte, neutrophil, and platelet counts across BMI groups. Given prior links between NLR and kidney injury [24], these findings suggest a potential inflammatory pathway linking obesity to renal dysfunction.
In our study, lipid parameters did not show significant differences across BMI categories, suggesting that increasing adiposity was not uniformly associated with dyslipidaemia, which is known to vary widely in obese populations depending on metabolic phenotype [25]. Renal function indices remained within normal ranges, although increasing urine albumin with higher BMI suggests early subclinical renal involvement, as previously reported in obesity-associated albuminuria linked to inflammation [26].
Our study has several strengths. Firstly, it is the first study in India that focuses only on Asian Indian phenotype, which may help reduce the influence of several known confounding factors that can adversely impact kidney function. Secondly, the study also employed a detailed evaluation of body composition parameters with early kidney injury markers which lead to better understanding of adiposity-related renal stress.
The study has a few limitations. Firstly, it has a cross-sectional design that limits the ability to prove a causal association or predict long-term renal outcomes. Secondly, the data reflect a single centre experience which may restrict generalizability of finding to other populations. Thirdly, we had encountered challenges in recruiting participants for Group D (BMI: >40 kg/m²) due to the difficulty of finding individuals with class III obesity who meet the inclusion criteria, particularly without comorbidities. As a result, the number of participants in enrolled in this group was limited to 25 instead of the intended 45.
Conclusion
In conclusion, this study observed higher serum NGAL levels in individuals with increasing degrees of obesity and identified a positive association between NGAL and measures of adiposity. These findings suggest that serum NGAL may reflect early renal stress in obesity, even in individuals without overt metabolic comorbidities such as diabetes mellitus, hypertension, or chronic kidney disease. However, given the cross-sectional nature of the study and the limited representation of individuals with class III obesity, these observations should be interpreted with caution. Prospective and longitudinal studies across diverse populations are warranted to clarify the temporal relationship between obesity and NGAL elevation.
Acknowledgements
We greatly appreciate the contribution of every single participant, and our research team (Mr. Shubham Awasthi, Mr. Keshav Vashisht, Mr. Divesh Kr. Sharma) for valuable assistance.
Abbreviations
- CKD
Chronic Kidney Disease
- ASMI
Appendicular skeletal muscle index
- BMI
Body Mass Index
- NGAL
Neutrophil gelatinase-associated lipocalin
- TIMP-2
Tissue Inhibitors of Metalloproteinases-2
- IGFBP-7
Insulin-like growth factor binding protein − 7
- ORG
Obesity-related Glomerulopathy
- NLR
Neutrophil/lymphocyte ratio
Authors’ contributions
Study design and conceptualization: AS, Data Acquisition: Dr Farheen Ahmed, Sheli Paul, Data Analysis: Dr Farheen Ahmed, Dr. Shivam Pandey, Intellectual inputs: Dr Naval K Vikram, Dr Piyush Ranjan, Dr Ranveer Singh Jadon, Dr. Ved Prakash Meena, Writing of original draft : Dr Amandeep Singh, Dr Farheen Ahmed, Sheli Paul, Writing, reviewing, editing: Dr Amandeep Singh Dr Farheen Ahmed, Dr Prayas Sethi , Dr Ranveer Singh Jadon, Dr Naval K Vikram, Dr Piyush Ranjan, Dr. Ved Prakash Meena, Dr Priyatma, Dr Shyam Prakash. All authors are critically reviewed the content and approved final version for publication.
Funding
The study was funded by Research Section of All India Institute of Medical Sciences (AIIMS), New Delhi, under Early Career Research Grant.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
Declarations
Ethics approval and consent to participate
This study adhered to ethical principles outlined in the Helinski Declaration (1964) and its later amendments. It was approved by the Institutional Ethics Committee of the All India Institute of Medical Sciences (AIIMS), New Delhi (Ref No: AIIMSA00780/02.02.2024, RP-35/2024).
Written informed consent was obtained from all participants included in the study. All data was anonymized for maintaining confidentiality.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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