Retrospective analysis of urinary tract stone composition in a Chinese ethnic minority colony based on Fourier transform infrared spectroscopy – Scientific Reports

Individual and clinical characteristics of the patients

A total of 1092 patients were included in the study, with 745 (68.2%) men and 347 (31.8%) women, resulting in a male-to-female ratio of 2.1:1. The age of the patients ranged from 3 to 85 years, with a mean age of 50.9 ± 12.8 years. There were no significant differences in age between males (50.6 ± 12.9 years) and females (51.4 ± 12.8 years) (t = − 1.027, v = 1090, p > 0.05). The distribution of urinary stone locations among the patients was as follows: 536 (49.1%) cases of renal calculi, 502 (46.0%) cases of ureteral calculi, 47 (4.3%) cases of bladder calculi, and 7 (0.6%) cases of urethral calculi. Moreover, 74 (6.8%) patients had diabetes mellitus (DM), 217 (19.9%) patients had hypertension (HTN), 205 (19.8%) patients had a family history (FH) of urinary stones, 230 (21.0%) patients had urinary infections (UTI), and 82 (7.5%) patients had hyperuricemia (HUA) (Fig. 1).

General information about the stone composition

Infrared spectra of substances are unique, akin to human fingerprints^13,14,15. Consequently, distinct components of urinary tract stones exhibit characteristic infrared spectra, indicating their specific compositions. By utilizing these characteristic spectra (Fig. 2), we conducted qualitative and quantitative analyses of different stone compositions. A total of 1092 stone samples underwent FT-IR analysis for stone composition. Quantitative analysis revealed that single-component samples constituted 457 (41.8%) cases, including 367 (33.6%) cases of COM, 66 (6.0%) cases of COD, 13(1.2%) cases of UA, and 11(1.0%) cases of Others (rare components: L-cystine, drug-induced stones). Stones composed of two components accounted for 453 (41.5%) cases, comprising 278 (25.4%) cases of COM + CaP, 47 (4.3%) cases of COM + COD, 54 (4.9%) cases of Struvite + CaP, 41(3.8%) cases of UA + COM, and 33(3.0%) cases of COD + CaP. Stones with three components were observed in 149 (13.6%) cases, including 92 (8.4%) cases of COM + COD + CaP, 46 (4.2%) cases of CaP + COM + COD, 7(0.6%) cases of CaP + COM + COD, and 4 (0.4%) cases of Struvite + CaP + COM. Additionally, stones with four components were found in 33(3.0%) cases, comprising 24 (2.2%) cases of Struvite + CaP + COM + COD and 9 (0.8%) cases of Struvite + COM + COD + CaP (Fig. 3).

Characteristic infrared spectra of different stone components. The red lines represent downward absorption peaks (negative peaks). The x-axis indicates the wavelength (cm⁻¹), representing the position of absorption peaks, while the y-axis shows transmittance (T%), indicating the absorption intensity. (A) Calcium oxalate monohydrate (COM) exhibits absorption peaks at 3480–3050 cm⁻¹, 1620 cm⁻¹, 1320 cm⁻¹, 950 cm⁻¹, 885 cm⁻¹, 780 cm⁻¹, and 655 cm⁻¹ in the infrared spectrum. The peaks at 1620 cm⁻¹ and 1320 cm⁻¹ are attributed to the C = O and O–H vibrations, respectively. The broad absorption peak at 3480–3050 cm⁻¹ corresponds to water molecule vibrations, which are split into five individual peaks. (B) In comparison, calcium oxalate dihydrate (COD) exhibits a broad absorption peak at 3460 cm⁻¹ in the infrared spectrum, without any splitting phenomenon. Additionally, there is no absorption peak at 665 cm⁻¹. (C) The infrared spectrum of calcium phosphate (CaP) shows absorption peaks at 1630, 1460, 1415, 1100–1000, 870, 605, 570, and 450 cm⁻¹. (D) Anhydrous uric acid’s infrared spectrum displays absorption peaks at 1650, 1590, 1400, 1345, 1300, 1120, 1025, 900, 785, 700, 620, and 575 cm⁻¹. (E) The infrared spectrum of struvite exhibits absorption peaks at 3500–3000, 1650, 1440, 1400, 1100–1000, 750, and 570 cm⁻¹. (F) Cystine stones exhibit absorption peaks mainly at 1600–1200, 1125, 1020, 965, 850, 780, 680, 610, and 530 cm⁻¹ in the infrared spectrum.

General overview of the composition of urinary stones. The error bars represent standard deviation. From A to D, respectively, it represents the number and proportion of single-component stones, two-component stones, three-component stones, and four-component stones in the total, as well as the number and proportion of males and females.

Based on the stone classification principle described above, we categorized all stones into five types: CaOx, CaP, ST, UA, and Others (Fig. 4A). CaOx was the most prevalent stone type, with a total of 834(76.4%) cases, including 623(57.1%) in males and 211 (19.3%) in females. It was followed by 102 (9.3%) cases of CaP, including 61(5.6%) cases in males and 41(3.7%) cases in females. A total of 91(8.3%) cases of ST were detected, comprising 27 (5.8%) cases in males and 64(2.5%) cases in females. Among the 54 (4.9%) cases of UA, 46 (4.2%) were male and 8 (0.7%) were female. Additionally, there were 11(1.0%) other rare stone types, including 8(0.07%) in males and 3(0.03%) in females, notably 3 of which were drug-induced stones in children.

Distribution of different urinary stone types. (A) The distribution of five stone types, with 843 cases of CaOx, 102 (9.3%) cases of CaP, 91(8.3%) cases of ST, 54 (4.9%) cases of UA, and 11(1.0%) other rare stone types. The error bars represent standard deviation. (B) The chi-square test for differences in the distribution of gender and the four main types of stones among the 1081 study subjects (χ² = 122.5, p < 0.001; Cramer’s V = 0.337, p < 0.05).

The relationship between gender and four types of urinary stones

Because the “Other” stone types are rare and inclusion in statistical models can produce extreme values that affect the accuracy of statistical results, we do not include this type in some statistical models for analysis. A total of 1081 cases were included in the statistical model, which analyzed the relationship between sex and four main types of stones (CaOx, CaP, ST, UA). The results show that any of the expected frequencies are greater than 5, and the chi-square test can be used, χ² = 122.5, p < 0.001, suggesting that the four main types of stones differ significantly from gender type. There is a moderately strong correlation between four different stone types and gender (Cramer’s V = 0.337, p < 0.05) (Fig. 4B). We conducted a more in-depth analysis of the above results using post hoc testing and assessed the relationship between the two categorical variables based on the adjusted standardized residuals (Adj.R). In the CaOx group, 623 (57.6%) were males and the Adj. R value of 8.5 indicated a significant association between CaOx stones and male gender, suggesting a higher likelihood of CaOx stones in male patients compared to females. Similarly, in the CaP group, there were 61 (5.6%) females, and the Adj.R value of 6.4 revealed a significant association between CaP stones and the female gender. Likewise, in the ST group, there were 64 (5.9%) females, and the Adj.R value of 8.2 also indicated a significant association between ST stones and female gender. These results consistently suggest a higher likelihood of CaP and ST stones in female patients compared to males (Fig. 5A).

The analysis of the relationship between different factors and the four main types of urinary stones (CaOx, CaP, ST, UA). (A) The heat map displays the normalized Adj.R, with higher convergence to red indicating a stronger association. The red color highlights that women are more likely to develop ST and CaP stones, while men are more prone to CaOx stones. Additionally, patients with UTI have a higher tendency to develop ST stones, and those with HUA are more likely to have UA stones. (B–F) Mosaic plots were used to display the results of Chi-square tests or Fisher’s exact tests, which were conducted to examine the differences between underlying diseases and urinary stone types, two categorical variables. Each color block represents a specific type of urinary stone, and the size of the block corresponds to the number of stones in that category. The specific test results are as follows: (B) DM vs NO-DM: χ² = 2.8, p = 0.418; Cramer’s V = 0.051, P > 0.05; (C) HTN vs NO-HTN: χ² = 4.6, P = 0.202; Cramer’s V = 0.065, P > 0.05; (D) UTI vs NO-UTI: χ² = 161.7, p < 0.001; Cramer’s V = 0.387, p < 0.001; (E) FH vs NO-FH: p = 0.037 < 0.05; Cramer’s V = 0.092,p > 0.05; (F) HUA vs NO-HUA: p < 0.001; Cramer’s V = 0.265, p < 0.001).

The relationship between DM, HTN, FH, UTI, HUA and urinary stone types

A total of 1081 cases were included in the statistical model, which examined the difference between DM, HTN, FH, UTI, HUA, and four main types of stones (CaOx, CaP, ST, UA). The results showed that for DM, HTN, and UTI, all expected frequencies were greater than 5, allowing the use of the chi-square test. From the perspective of DM (χ² = 2.8, p = 0.418; Cramer’s V = 0.051, p > 0.05) and HTN (χ² = 4.6, p = 0.202; Cramer’s V = 0.065, p > 0.05), there was no statistically significant difference, indicating that the presence or absence of DM/HTN did not significantly affect the type of stone (Fig. 5B,C). However, among the 1081 study participants, 230 had UTI, and the data (χ² = 161.7, p < 0.001) indicated a significant difference in the distribution of the four different stone types based on the presence or absence of UTI. There was a moderately strong correlation between four different stone types and UTI, with Cramer’s V = 0.387, P < 0.001 (Fig. 5D). Further post hoc testing revealed that patients with UTI were more likely to develop CaP (Adj.R = 9.0) and ST (Adj.R = 8.0) stones (Fig. 5A).

Among the 1081 study participants, 205 had FH, and 82 had HUA. Due to expected frequencies of less than 5, Fisher’s exact test was chosen. The results indicated a significant difference between FH and the four different stone types (p < 0.05), but there was no strong correlation between them (Cramer’s V = 0.092, p > 0.05) (Fig. 5E). Furthermore, the test results showed a clear difference between HUA and different types of stones (p < 0.001), and there was a weak correlation between four different stone types and HUA, with Cramer’s V = 0.26, p < 0.001 (Fig. 5F). Post hoc testing revealed that patients with Hyperuricemia were more likely to develop UA stones (Adj.R = 8.4) (Fig. 5A).

The relationship between age and urinary stone types

The relationship between age and urinary stone types was examined by dividing all study participants into 9 age strata, each representing a 10-year age group. This division allowed for a comprehensive analysis of the association between age and different stone compositions. Using Kruskal–Wallis tests, we found a statistically significant difference between the various age groups and stone types (H = 78.388, p < 0.001) (Fig. 6A). Further multiple comparisons were performed using the Z test with Bonferroni-adjusted p-values, revealing specific patterns. The proportion of CaOx cases was higher in the age groups of 21–30 years (compared to age groups 1–10, 51–60, 61–70, 71–80, 81–90 years, p < 0.05), 31–40 years (compared to age groups 1–10, 51–60, 61–70 years, p < 0.05), and 41–50 years (compared to age groups 1–10, 51–60, 61–70 years, p < 0.05) compared to other types of stones. This suggests that individuals in these age groups are more likely to develop CaOx stones.

Distribution of different stone types across age groups and stone locations. (A) The distribution of different stone types across age groups. Kruskal–Wallis tests (H = 78.388, p < 0.001) revealed significant differences between different age groups and stone types. (B) Significant differences in the distribution of different stone types among different anatomical sites (Fisher’s exact test, p < 0.001; Cramer’s V = 0.108, p < 0.001). (C) The normalized Adj.R values were represented on a heat map, where a higher convergence to red indicates a stronger association. The red color indicates that ST is more likely to be found in the kidney, CaOx in the ureter, UA in the bladder, and rare stones in the urethra, relative to other stone types.

In the 51–60 and 61–70 age groups, the proportion of ST cases (compared to age group 41–50 years, p < 0.05; 61–70 years compared to age group 41–50 years, p < 0.05) and UA cases (compared to age groups 31–40, 41–50 years, p < 0.05; 61–70 years compared to age group 41–50 years) was higher than other stone types. This indicates a higher likelihood of developing ST and UA stones in these age groups. Furthermore, we observed that rare stones were more likely to affect children aged 1–10 (compared to age groups 31–40, 41–50, 51–60, p < 0.05). Additionally, individuals aged 81–90 (compared to age group 31–40, p < 0.05) were most likely to develop CaP stones (Table 1).In summary, age appears to play a significant role in the distribution of different stone types, with distinct patterns observed in various age groups.

The relationship between stone location and urinary stone types

In the analysis of 1092 stone samples, due to the expected frequencies being less than 5, we opted for Fisher’s exact test. The results revealed a significant difference (p < 0.001) in the distribution of stone types based on their locations. Furthermore, there was a weak correlation (Cramer’s V = 0.108, p < 0.001) between different stone types and their locations (Fig. 6B). Subsequent post hoc testing indicated that ST tended to occur more frequently in the kidneys (Adj.R = 3.1), UA tended to occur more in the bladder (Adj.R = 3.2), and rare stones tended to occur in the urethra (Adj.R = 3.5) (Fig. 6C).

Relative risk analysis for non-CaOx Stone types using unordered multi-classification logistic regression

In this study, we employed an unordered multi-classification Logistic regression model with four different stone types (CaOx, CaP, ST, UA) as the unordered multi-class response variables. Simultaneously, we considered six statistically significant factors (gender, age, family history, urinary tract infection, hyperuricemia, and stone location) from the previous research as independent variables. As CaOx had the highest number of cases, it served as the reference category. The Logistic regression model was utilized to predict and assess the relative risk factors for stone types other than CaOx in specific populations. Compared to CaOx, we observed that female gender (RR= 3.087, 95% CI 1.944–4.903) and urinary tract infection (RR= 5.272, 95% CI 3.219–8.635) were relative risk factors for predicting CaP. Similarly, female gender (RR= 4.871, 95% CI 2.944–8.060) and urinary tract infection (RR= 4.921, 95% CI 2.910–8.323) were relative risk factors for predicting ST. Additionally, family history (RR= 2.539, 95% CI 1.286–5.012) and hyperuricemia (RR= 7.729, 95% CI 4.010–14.898) were relative risk factors for predicting UA (Tables 2, 3).This model enables us to understand the relative risk factors associated with specific factors and different stone types, thus providing better support for predicting and implementing personalized treatment approaches for individuals with specific urinary stone types.