Successful Diabetic Control as Measured by Hemoglobin A1c Is Associated with Lower Urine Risk Factors for Uric Acid Calculi

Abstract

Introduction and Objectives:

To examine the association of glycemic control, including strict glycemic control, with 24-hour urine risk factors for uric acid and calcium calculi.

Materials and Methods:

With institutional review board (IRB) approval, we identified 183 stone formers (SFs) with 459 twenty-four-hour urine collections. Hemoglobin A1c (HgbA1c) measures were obtained within 3 months of the urine collection. Collections were categorized into normoglycemic (NG, HgbA1c < 6.5) and hyperglycemic (HG, HgbA1c ≥ 6.5) cohorts; 24-hour urine parameters were compared. The NG cohort was further divided into patients with and without a history of diabetes mellitus (DM) type 2. Variables were analyzed using chi-square, Welch's t-test and multivariate linear regression to adjust for clustering, body mass index (BMI), age, gender, thiazide use, and potassium citrate use.

Results:

Patients in the HG group were older with higher BMI. Multivariate analysis of the total study population revealed that hyperglycemia correlated with lower pH, higher uric acid relative saturation (RS), lower brushite RS, and higher citrate. NG SFs with and without a history of DM had similar risk factors for uric acid stone formation. Among NG SFs, those with DM had higher urine calcium and calcium oxalate RS than those without DM. However, this difference may be related to other factors since neither parameter correlated with DM on multivariate regression (p > 0.05).

Conclusions:

Successful glycemic control may be associated with reduced urinary risk factors for uric acid stone formation. Patients with well-controlled DM had equivalent risk factors to those without DM. Glycemic control should be considered a target of the multidisciplinary medical management of stone disease.

Introduction

Over the past two decades, increased prevalence of both urinary stone disease¹ and diabetes mellitus (DM) type 2² is observed in the United States. This temporal parallelism is supported by epidemiological evidence that DM is independently associated with higher risk of nephrolithiasis.^3

–9 The proposed mechanisms of this association include insulin resistance and impaired glucose metabolism causing changes in the urinary metabolic profile due to altered renal acid–base metabolism as well as deficient ammoniagenesis and excretion.¹⁰ Previous studies identified associations between hyperglycemia and changes in urinary risk factors for kidney stones based on binary categorization of patients with or without DM.^4,11

–14

The results of epidemiological and pathophysiology studies suggest that risk of kidney stones should increase with the severity of the insulin resistance. Studies report increasing odds of having kidney stones with increasing number of metabolic syndrome traits⁹ and DM severity.^15,16 Few recent studies, however, have compared the effects of glycemic control on 24-hour urine collection parameters in stone formers (SFs) with DM.^17,18 It remains unknown how the effects of strict glycemic control affect urinary stone formation risk factors. In this study, we sought to examine the association of hemoglobin A1c (HgbA1c), a measure of long-term DM control, and the effects of strict glycemic control with 24-hour urine risk factors for uric acid and calcium calculi.

Methods

Study design

With institutional review board approval, we retrospectively reviewed 3105 twenty-four-hour urine collections for 827 patients at a tertiary care academic medical center metabolic stone clinic. All patients were documented SFs; inclusion in this study was not limited by stone composition. To exclude incomplete urine collections, we limited the analysis to participants whose 24-hour urine creatinine values were >600 mg/day. We identified 183 SFs with 459 acceptable 24-hour urine collections and who had HgbA1c measures from within 3 months of the urine collection. HgbA1c is a blood test used to screen for DM, and to monitor patient compliance with therapy and/or therapeutic response to treatment in patients with DM. HgbA1c rises in response to hyperglycemia and reflects the percentage of glycated hemoglobin in the past 100 days. The electronic medical record was reviewed to obtain demographics, comorbidities, laboratory results, and medications. Patients with type 2 DM were identified from the information provided in clinical notes, and this was corroborated by documentation of treatment, including clinical nutrition management, use of oral hypoglycemic agents, and/or use of insulin.

Patients provided 24-hour urine collections that were processed by Quest Diagnostics (Valencia, CA) or Litholink (Lab Corp Specialty Testing Group, Chicago, IL). Standard urinary parameters were evaluated, including calcium, oxalate, uric acid, citrate, total volume, sodium, magnesium, potassium, creatinine, and sulfate. Each commercial laboratory uses proprietary software to calculate relative saturation (RS; Quest Diagnostics) or supersaturation (SS; Litholink) indices, and could not provide formulas for these calculations when we contacted them. Thus, saturations could not be compared between laboratories. Statistical comparisons for RS and SS indices were calculated separately for urine collections analyzed by Quest Diagnostics and Litholink.

Urine collections were categorized by glycemic status based on HgbA1c as normoglycemic (NG, HgbA1c < 6.5%) and hyperglycemic (HG, HgbA1c ≥ 6.5%). According to the World Health Organization, a HbA1c of 6.5% is recommended as the cut point for diagnosing diabetes. The NG cohort was further divided into patients with a history of diabetes (under excellent glycemic control) and without a history of DM. Groups were compared based on age, gender, body mass index (BMI), potassium citrate use, thiazide use, and 24-hour urine collection parameters. We conducted two separate analyses, treating HgbA1c as a binary “hyperglycemic vs normoglycemic” variable in the univariate chi-square or t-tests in an effort to analyze these results in reference to clinical practice, where patients may be diagnosed in a binary fashion, as well as treating HgbA1c as a continuous variable in the multivariate regression in an effort to analyze correlations between glycemic control and changes in urinary parameters. Univariate and multivariate analyses were repeated excluding patients on potassium citrate therapy to assess effects without supplemental citrate.

Statistical analysis

Values are reported as median (interquartile range [IQR]). For univariate analysis, categorical variables were compared using the chi-square test, and continuous variables were compared using Welch's unequal variance t-test. All urinary constituents were found to have a non-normal distribution, and a log or square-root transformation was used to reduce variance. After transformation, multivariate linear regression was used to assess correlations between urine parameter, and HgbA1c, history of DM, or insulin use. These models were used to adjust for possible confounding variables, including BMI, age, gender, thiazide use, and potassium citrate use. To account for clustering of multiple urine collections from some patients, the variance–covariance matrix clustered sandwich estimator was used to obtain robust estimates for the 95% confidence intervals for multivariate regression models. All tests were two sided with significance considered at p < 0.05. Statistical analysis was performed using Stata^® (version 15.0, College Station, TX).

Results

Comparison of NG and HG stone formers

Urine collections were made from predominantly male patients (273/459, 59%). Median age at time of collection was 62 years (IQR 55–67 years), and median BMI was 32.7 (IQR 28.4–37.6) kg/m². Median interval between HgbA1c and 24-hour urine collection was 36 days (IQR 15–56 days), demonstrating the close relationship we were striving for in our cohort. Two hundred seven urine collections (45%) were deemed NG, and 55% were allocated to the HG group. Collections from HG SFs were more likely to be from older patients (median 63 vs 60 years, p = 0.0002) with a higher BMI (median 34.0 vs 31.6 kg/m², p = 0.0038). Demographic characteristics for NG and HG SFs are listed in Table 1.

Table 1.

Demographic Data from Normoglycemic and Hyperglycemic Groups

	NG (HgbA1c < 6.5)	HG (HgbA1c ≥ 6.5)	p
Total 24-hour urinalyses, n (%)	207 (45)	252 (55)	—
HgbA1c, median (IQR)	6.0 (5.6–6.2)	7.4 (6.9–8.2)	<0.0001
Age (years), median (IQR)	60 (52–66)	63 (56–69)	0.0002
Gender, male, n (%)	114 (55)	159 (63)	0.081
BMI (kg/m²), median (IQR)	31.6 (27.6–35.1)	34.0 (29.7–38.9)	0.0038
Potassium citrate use, n (%)	47 (23)	69 (27)	0.25
Thiazide use, n (%)	78 (38)	114 (45)	0.10
Oral antihyperglycemic use, n (%)	75 (36)	209 (82)	<0.001
Insulin use, n (%)	13 (6)	80 (32)	<0.001

BMI = body mass index; HG = hyperglycemic; HgbA1c = hemoglobin A1c; IQR = interquartile range; NG = normoglycemic.

HG SFs had higher propensity for uric acid stone formation based on urine parameters. On univariate analysis, HG SFs had lower urine pH (median 5.9 vs 6.3, p < 0.0001, Table 2) and higher uric acid RS (Quest Diagnostics; median 1.34 vs 0.66, p < 0.0001). After multivariate analysis controlling for clustering, BMI, age, gender, thiazide use, and potassium citrate use, hyperglycemia inversely correlated with pH (p = 0.002) and directly correlated with uric acid RS (p < 0.001 for Quest Diagnostics). Similar results were found when patients on potassium citrate were excluded (pH p = 0.013; uric acid RS p = 0.002 for Quest Diagnostics and uric acid SS p = 0.005 for Litholink; Supplementary Tables S1 and S2; Supplementary Data are available online at www.liebertpub.com/end).

Table 2.

Twenty-Four-Hour Collections from Normoglycemic and Hyperglycemic Groups

Urine parameter	NG (HgbA1c < 6.5, n = 184)	HG (HgbA1c ≥ 6.5, n = 224)	p	MV regression p-value ^a
Calcium (mg/day)	205 (124–293)	196 (106–327)	0.82	0.62
Oxalate (mg/day)	40 (31–51)	45 (35–60)	0.02	0.68
Uric acid (mg/day)	562 (432–761)	587 (419–784)	0.31	0.98
Citrate (mg/day)	606 (374–843)	694 (369–1178)	<0.001	0.012
pH	6.3 (5.8–6.8)	5.9 (5.5–6.5)	<0.0001	0.002
Volume (L/d)	2.0 (1.5–2.8)	2.0 (1.6–6.5)	0.03	0.16
Sodium (mEq/day)	157 (112–205)	186 (131–234)	0.003	0.19
Phosphorus (mg/day)	867 (626–1146)	835 (131–234)	0.88	0.17
Magnesium (mg/day)	115 (79–158)	106 (74–139)	0.05	0.34
Calcium oxalate SS
Q	1.65 (1.09–2.28)	1.85 (1.16–2.67)	0.03	0.41
L	6.40 (4.74–7.72)	4.81 (2.85–6.83)	0.16	0.015
Brushite SS
Q	1.46 (0.61–2.79)	0.92 (0.28–1.82)	0.0008	0.20
L	1.20 (0.69–1.90)	0.54 (0.20–0.95)	0.01	0.43
Sodium urate SS	1.44 (0.90–2.51)	1.71 (0.90–3.10)	0.67	0.70
Uric acid SS
Q	0.66 (0.19–1.71)	1.34 (0.49–2.70)	<0.0001	<0.001
L	0.16 (0.09–0.96)	0.64 (0.26–1.48)	0.03	0.52
Potassium (mEq/day)	65 (50–82)	66 (49–85)	0.86	0.11
Creatinine (mg/day)	1466 (1146–1889)	1537 (1149–2028)	0.44	0.51
Sulfate (mmol/day)	18 (12–26)	21 (14–31)	0.09	0.75

Values reported as median (IQR). Sodium urate SS is not reported for Litholink 24-hour urinalyses.

MV linear regression used to adjust for BMI, gender, age, thiazide use, and potassium citrate use.

L = Litholink; MV = multivariate; Q = Quest; SS = supersaturation.

Hyperglycemia posed equivocal risk between groups for calcium stone formation. Urine calcium (UCa) measures were similar (median 196 vs 205 mg/day, p = 0.82, Table 2), but HG SFs had slightly higher urinary oxalate (median 45 vs 40 mg/day, p = 0.02) with resultant higher calcium oxalate (CaOx) RS (Quest Diagnostics; median 1.85 vs 1.65, p = 0.03). While statistically different, the clinical significance of these findings is questionable as neither group had a median CaOx RS >2.0. Furthermore, neither UCa nor CaOx SS was correlated with HgbA1c on multivariate analysis (p > 0.05). Hyperglycemia was associated with a reduced risk for brushite stone formation (Quest Diagnostics; brushite RS 0.92 vs 1.46, p = 0.0008; multivariate p = 0.20). Similar trends for UCa and brushite RS were observed when excluding patients on potassium citrate therapy (Supplementary Table S2).

HG SFs had higher urine citrate (median 694 vs 606 mg/day, p < 0.001, Table 2), and urine citrate levels directly correlated with HgbA1c on multivariate analysis (p = 0.012).

NG subgroup analysis

Twenty-four-hour urine collections from NG patients were further divided into two groups: 103 (50%) from patients without DM and 104 (50%) from patients with DM under excellent control (i.e., HgbA1c < 6.5). NG SFs with DM had a higher median BMI (32.9 vs 30.6 kg/m², p = 0.02, Table 3) and had a lower prevalence of potassium citrate use (14% vs 31%, p = 0.004) as well as thiazide use (31% vs 45%, p = 0.04). NG SFs with DM had higher UCa (median 221 vs 185 mg/day, p = 0.04) and CaOx RS (Quest Diagnostics; 1.84 vs 1.45, p = 0.03, Table 4). However, multivariate regression revealed no significant correlations between any urinary parameters and DM when adjusting for BMI, gender, age, thiazide use, and potassium citrate use. When excluding patients on potassium citrate, DM diagnosis directly correlated with higher urine citrate (p = 0.002; Supplementary Tables S3 and S4) and creatinine (p = 0.036).

Table 3.

Demographic Data from Normoglycemic Stone Formers Without and With Diabetes Mellitus

Data	NG SFs without DM	NG SFs with DM	p
Total 24-hour urinalyses, n (%)	103 (50)	104 (50)	—
HgbA1c, median (IQR)	5.7 (5.5–6.0)	6.2 (5.9–6.3)	<0.0001
Age (years), median (IQR)	60 (51–65)	60 (55–66)	0.45
Gender, male, n (%)	53 (51)	61 (59)	0.30
BMI (kg/m²), median (IQR)	30.6 (27.5–33.5)	32.9 (28.0–37.0)	0.02
Potassium citrate use, n (%)	32 (31)	15 (14)	0.004
Thiazide use, n (%)	46 (45)	32 (31)	0.04

DM = diabetes mellitus; SFs = stone formers.

Table 4.

Twenty-Four-Hour Collections from Normoglycemic Stone Formers Without and With Diabetes Mellitus

Urine parameter	NG SFs without DM (n = 103)	NG SFs with DM (n = 104)	p	MV p-value ^a
Calcium (mg/day)	185 (116–260)	221 (146–314)	0.04	0.17
Oxalate (mg/day)	38 (30–48)	41 (33–51)	0.90	0.82
Uric acid (mg/day)	569 (432–759)	572 (432–786)	0.65	0.98
Citrate (mg/day)	605 (390–843)	619 (335–828)	0.87	0.89
pH	6.4 (5.8–6.8)	6.3 (5.8–6.8)	0.34	0.85
Volume (L/d)	1.9 (1.5–2.8)	2.0 (1.4–2.8)	0.49	0.92
Sodium (mEq/day)	148 (105–192)	161 (120–221)	0.09	0.53
Phosphorus (mg/day)	874 (618–1167)	864 (628–1119)	0.37	0.21
Magnesium (mg/day)	114 (84–155)	119 (74–164)	0.57	0.85
Calcium oxalate SS
Q	1.45 (0.94–2.08)	1.84 (1.19–2.43)	0.03	0.20
L	5.94 (4.34–8.07)	6.40 (6.12–6.81)	0.41
Brushite SS
Q	1.47 (0.53–2.61)	1.44 (0.82–2.98)	0.30	0.42
L	1.25 (0.69–2.26)	1.06 (0.76–1.75)	0.25
Sodium urate SS	1.44 (0.86–2.74)	1.44 (0.95–2.50)	0.92	0.71
Uric acid SS
Q	0.60 (0.22–1.40)	0.72 (0.18–1.92)	0.35	0.84
L	0.15 (0.10–0.96)	0.33 (0.09–0.44)	0.62
Potassium (mEq/day)	63 (48–82)	69 (52–81)	0.98	0.44
Creatinine (mg/day)	1480 (1095–1875)	1450 (1210–1896)	0.68	0.21
Sulfate (mmol/day)	18 (12–28)	18 (12–25)	0.28	0.38

Values reported as median (IQR).

MV linear regression used to adjust for BMI, gender, age, and thiazide use. Too small of a sample of SFs with Litholink 24-hour urinalyses for MV regression.

Discussion

The aim of this study was to investigate associations of glycemic control with urinary risk factors for stone formation. We found that the degree of hyperglycemia, as reflected by higher HgbA1c, was associated with increased risk of uric acid stone formation evidenced by lower urine pH and higher uric acid RS/SS. Most importantly, however, we found that SFs with well-controlled DM had fewer uric acid stone formation risk factors, equivalent to those of patients without DM. Our findings support promoting glycemic control as a target of the multidisciplinary medical management of stone disease, particularly for uric acid stones.

The systemic sequelae of insulin resistance, hyperglycemia, and compensatory hyperinsulinemia, as well as oxidative stress and inflammation in DM, may promote alterations in urine chemistry that increase propensity for uric acid stones.¹⁰ DM is associated with derangements in renal ammoniagenesis and ammonia excretion^19,20 resulting in increased urinary acidification,⁴ which favors precipitation of uric acid calculi.^11,14,19,21 The influence of hyperglycemia itself on uric acid stone risk can be difficult to study because obesity, which is a risk factor for DM, is also inked to acidic urine.²² In this study, we found that hyperglycemia correlates with lower urine pH after adjustment for potentially confounding variables, including gender, age, BMI, thiazide use, and potassium citrate use.

A few other investigations have also sought to determine the relationship between the severity of hyperglycemia and stone risk. Fram and colleagues identified 188 patients with 24-hour urine collections and HgbA1c measurements, and reported correlations between HgbA1c and 24-hour urinary citrate, creatinine, and volume but not pH.¹⁷ However, the authors used dipstick urine pH as the measure of urinary acidity, which is notoriously inaccurate compared with values obtained through pH electrodes and may explain the differences in the findings from our study.²³ In addition, the authors did not report RS/SS, and also used broad inclusion criteria that permitted a time lapse of 180 days between 24-hour urine collection and HgbA1c measurement. Since HgbA1c correlates with the average blood sugar in the past 100 days, such a liberal time frame may have resulted in the urine and HgbA1c tests occurring during periods of vastly different glycemic control.

Torricelli and coauthors reviewed urine chemistries and HgbA1c values in a large number of patients with DM (1831 patients), and found a relationship between hyperglycemia and urine pH.¹⁸ While the article does not report the time frame between HgbA1c and 24-hour urine assessment, the authors reported that for each point rise in HgbA1c, urine pH went down by 0.066. Since the objective of that study was different than ours, namely to identify whether differences in medication management strategy were associated with differences in stone risk, the authors did not report all urine variables or any RS/SS data, other than suggesting that they found no association between HgbA1c and urinary calcium, oxalate, or citrate.

One of the strengths of our investigation compared with the others is the short 3-month time interval between 24-hour urine and HgbA1c measurements, intended to maximize the chances that the urine chemistry correlated with current metabolic conditions. This is a challenging inclusion criterion to apply to any data set. Since the urologists or nephrologists ordering 24-hour urine collections are rarely the same providers managing a patient's DM, the odds that these two laboratory tests, both typically ordered about once a year, will occur within the same 3-month interval is low. Indeed, of the 3105 twenty-four-hour urine collections we reviewed, only 459 had an available HgbA1c measurement that met our 3-month requirement. We felt this strict limitation was necessary, however, as HgbA1c is known to vary seasonally even in the absence of alteration of medications for DM.²⁴

The same reasoning justifies our not utilizing stone composition data in our study. Stones may form over a long period of time, and the composition therefore cannot be linked to a specific period of glycemic control the way that 24-hour urine chemistries can. In other words, even if a patient has a stone analysis available, there is no way to know exactly what glycemic control conditions were present during the stone formation. Patients with diabetes may achieve dramatic, lasting improvement in their diabetic control but pass a uric acid stone that had been in the kidney for years. Does that mean that excellent glycemic control is associated with uric acid stone formation? Of course not. Though as clinicians our endpoint of interest is finding ways to reduce actual stone formation, these sorts of associations cannot be made easily when it comes to stone composition. Thus, we have chosen to use 24-hour urine chemistries and the propensity for stone formation as endpoints instead since the daily conditions that result in them can be linked to a much smaller time period.

Limitations of our study include the retrospective observational and cross-sectional nature of the study, which prevents ascertainment of temporal associations and necessitates further prospective study. Also, as obesity is independently correlated with higher frequency of nephrolithiasis,^8,10,25 we considered this confounding factor when designing multivariate analyses. In accordance with best practice guidelines, patients in our cohort sometimes had multiple 24-hour urinalyses and HgbA1c pairs available for analysis. Selecting just one of these for inclusion could produce bias, so we included all qualifying laboratory pairs but rigorously adjusted for this in our multivariate statistical analysis, as described above. Finally, the retrospective nature of the study prohibited evaluation for multiple dietary factors and limited the study population to only known SFs without a control non-SF group. Further prospective comparison of urinary risk factors of SFs with known glycemic history and dietary history in relation to stone events would further elucidate the effect of glycemic control on preventing future stone episodes.

Conclusion

Our study suggests that successful glycemic control may be associated with reduced urinary risk factors for uric acid stone formation. Excellent glycemic control may negate the increased risk of uric acid nephrolithiasis typically associated with DM and confer lower risk that is comparable with SFs without DM. Our findings support promotion of glycemic control as a target of the multidisciplinary medical management of stone disease. These findings may be important when counseling patients with DM about their treatment options for kidney stones. Simplifying and streamlining care with a single strategy to improve multiple health risks could improve patient compliance.

Footnotes

Acknowledgments

This article is the result of work supported with resources and the use of facilities at the William S. Memorial Veterans Hospital. The contents do not represent the views of the U.S. Department of Veterans Affairs or the U.S. Government.

Author Disclosure Statement

No competing financial interests exist.

Abbreviations Used

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