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Original research
Does chronic hyperglycaemia increase the risk of kidney stone disease? results from a systematic review and meta-analysis
  1. Robert Geraghty1,
  2. Abdihakim Abdi2,
  3. Bhaskar Somani3,
  4. Paul Cook4,
  5. Paul Roderick5
  1. 1Urology, Freeman Hospital, Newcastle upon Tyne, UK
  2. 2University of Southampton, Southampton, Hampshire, UK
  3. 3Urology, University Hospital Southampton NHS Trust, Southampton, UK
  4. 4Clinical Biochemistry, University Hospital Southampton, Southampton, Hampshire, UK
  5. 5Health Care Research Unit, University of Southampton, Southampton, UK
  1. Correspondence to Dr Robert Geraghty; rob.geraghty{at}


Design Systematic review and meta-analysis of observational studies was performed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for studies reporting on diabetes mellitus (DM) or metabolic syndrome (MetS) and kidney stone disease (KSD).

Objective To examine the association between chronic hyperglycaemia, in the form of DM and impaired glucose tolerance (IGT) in the context of MetS and KSD.

Setting Population-based observational studies. Databases searched: Ovid MEDLINE without revisions (1996 to June 2018), Cochrane Library (2018), CINAHL (1990 to June 2018),, Google Scholar and individual journals including the Journal of Urology, European Urology and Kidney International.

Participants Patients with and without chronic hyperglycaemic states (DM and MetS).

Main outcome measures English language articles from January 2001 to June 2018 reporting on observational studies. Exclusions: No comparator group or fewer than 100 patients. Unadjusted values were used for meta-analysis, with further meta-regression presented as adjusted values. Bias was assessed using Newcastle-Ottawa scale.

Results 2340 articles were screened with 13 studies included for meta-analysis, 7 DM (three cohort) and 6 MetS. Five of the MetS studies provided data on IGT alone. These included: DM, n=28 329; MetS, n=31 767; IGT, n=12 770. Controls: DM, n=5 89 791; MetS, n=1 78 050; IGT, n=2 93 852 patients. Adjusted risk for DM cohort studies, RR=1.23 (0.94 to 1.51) (p<0.001). Adjusted ORs for: DM cross-sectional/case-control studies, OR=1.32 (1.21 to 1.43) (p<0.001); IGT, OR=1.26 (0.92 to 1.58) (p<0.0001) and MetS, OR=1.35 (1.16 to 1.54) (p<0.0001). There was no significant difference between IGT and DM (cross-sectional/case-control), nor IGT and MetS. There was a moderate risk of publication bias. Statistical heterogeneity remained significant in adjusted DM cohort values and adjusted IGT (cross-sectional/case-control), but non-signficant for adjusted DM (cross-sectional/case-control).

Conclusion Chronic hyperglycaemia increases the risk of developing kidney stone disease. In the context of the diabetes pandemic, this will increase the burden of stone related morbidity and mortality.

PROSPERO registration number CRD42018093382

  • urolithiasis
  • diabetes & endocrinology
  • other metabolic, e.g. iron, porphyria

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Strengths and limitations of this study

  • Largest systematic review and meta-analysis examining the risk of chronic hyperglycaemic states and kidney stone disease (KSD), with bias analysis.

  • Meta-analysis of cohort studies examining diabetes mellitus (DM) demonstrates an increased risk of KSD of 1.23 (0.94 to 1.51) (p<0.001) over the general population.

  • There was a moderate risk of publication bias.

  • Statistical heterogeneity remained significant in adjusted DM cohort values and adjusted impaired glucose tolerance.

  • No data on stone type.


Kidney stone disease (KSD) is a painful and costly condition1 where precipitates of normal urinary solutes aggregrate to form stones of varying sizes and compositions.2 Incidence of acute urolithiasis is rising worldwide,3–6 with corresponding rises in surgical treatment rates7 and morbidity8 9 although mortality has declined.8 10 Five-year recurrence rates have been reported as high as 50%.11 Long-term problems associated with recurrent KSD are decreased quality of life, missed work days,12 disabling pain, need for repeated operations, complications including infection and acute kidney injury,13 14 as well as long-term increased risk of developing chronic kidney disease.15

Patients with diabetes mellitus (DM)16 and metabolic syndrome (MetS)17 have been identified as carrying a higher risk of developing KSD. The global prevalence of both conditions has risen to pandemic levels9 18 seemingly in parallel with KSD.19 There is overlap between the two conditions, with impaired glucose tolerance (IGT), or pre-diabetes being one of the five components of the ‘metabolic syndrome’.20 Although the pathophysiology with respect to KSD is yet to be definitively described, patients with either MetS or DM have been shown to have increased urinary acidification and produce more uric acid stones than controls. Notably, with rising body mass index (BMI) in both diabetic and non-diabetic patients, the incidence of uric acid stones rises, while calcium oxalate stones fall.21 22

Previous systematic reviews have examined either DM16 or MetS17 23 in isolation. These studies performed either no meta-analysis,17 or else their heterogeneity/sensitivity analyses were limited.16 23 Given the overlap between the two conditions we aimed to perform a systematic review and meta-analysis of the existing literature on both DM and MetS with complete sensitivity, bias and heterogeneity analyses.

Evidence acquisition

Search strategy and study selection

Population – Chronic hyperglycaemics (diabetes mellitus, impaired glucose tolerance in the context of metabolic syndrome) and those with metabolic syndrome.

Comparator – Those without hyperglycaemia (DM/IGT) or metabolic syndrome, respectively.

Outcome – KSD – all compositions.

Study design – Systematic review and meta-analysis of published observational studies (cohort, case-control and cross-sectional).

Inclusion criteria

  1. All articles written in the English language.

  2. Adults (>18 years).

  3. All articles reporting on risk of developing kidney stone disease in diabetes mellitus (type 1 and type 2) in comparison to general population.

  4. All articles reporting on risk of developing kidney stone disease in patients with metabolic syndrome in comparison to general population.

  5. Risk in risk ratio (RR), HR, OR or prevalence ratio (PR) with 95% CIs.

Exclusion criteria

  1. Older studies using the same data as a more recent study – longest follow-up used.

  2. Studies exclusively using patients with kidney stone disease – unable to calculate risk.

  3. Studies with less than 100 patients – likely to be underpowered.

The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.24 The search strategy was conducted to find relevant studies from Ovid MEDLINE without revisions (1996 to June 2018), Cochrane Library (2018), CINAHL (1990 to June 2018),, Google Scholar and individual journals including the Journal of Urology, European Urology and Kidney International. The review was registered prospectively with PROSPERO.

Terms used included: ‘Diabetes’, ‘Diabetes mellitus’, ‘metabolic syndrome’, ‘urolithiasis’, ‘nephrolithiasis’, ‘kidney’, ‘uret*’, ‘ston*’, ‘calcul*’. Boolean operators (AND, OR) were used to refine the search.

The search was limited to English language articles between January 2001 and June 2018. Only published data were used.

Two reviewers (RG and AA) identified all studies. All studies that appeared to fit the inclusion criteria were included for full review. Each reviewer independently selected studies for inclusion in the review (see figure 1). If there was disagreement, PR and BS made final decision on inclusion.

Figure 1

PreferredReporting Items for Systematic Reviews and Meta-Analyses flow diagram for article selection. DM, diabetes mellitus; KSD, kidney stone disease.

Data extraction and assessment of quality

The following variables were extracted from each study: first author, year of publication, type of study, sample size, age, country, male:female ratio, ascertainment of DM/IGT/MetS/KSD, type of DM, number of patient reporting/presenting with stone disease for diabetes mellitus, metabolic syndrome and specifically IGT in the context of MetS (given the common mechanism – hyperglycaemia and insulin resistance).

Risk of KSD in RR, HR, OR or PR with 95% CIs was also extracted. HR and RR, and OR and PR, were considered the same and are presented as RR and OR, respectively. Unadjusted and adjusted risk values were extracted from the studies. Adjustment factors were recorded. If adjusted values were missing then the study was removed from the adjusted meta-analysis.

Cross-sectional and case-control studies were pooled as there were no case-control studies for MetS, and two case-control studies for DM, only one of which gave adjusted values.

Data were collated using Microsoft Excel (V.12.2.4). Level of evidence was assessed and study bias was analysed using the Newcastle-Ottawa bias assessment tool.25

Data sharing

Data has been uploaded to PROSPERO or can be obtained, on reasonable request, by emailing the corresponding author.

Statistical methods

Risk is presented with a 95% CI as RR for cohort studies and OR for case-control (CaCo) and cross-sectional (XS) studies. Statistical heterogeneity was tested for using I2, Tau2 and Cochran’s Q. P values <0.05 were considered statistically significant, I2 values were interpreted according to chapter 9.5.2 of the Cochrane Handbook. Heterogeneity was also tested with ‘leave-one-out’ analyses. Publication bias was assessed with Egger’s test and ‘trim and fill’ analysis. Meta-regression analysis was performed, adjusting for age and gender. Student's t-statistic is used for df.

Statistical analyses and figures were generated in R (R foundation for statistical computing, Vienna, Austria) with the metafor package.26

Evidence synthesis

Fifteen studies were included in the systematic review from an initial search total of 2340 (see figure 1). Articles excluded on the basis of title were 2301, 15 on the basis of abstract and 15 on reading the full text. This left 13 studies, 7 examining DM and 6 examining IGT in the context of MetS. Inter-rater reliability as assessed by Cohen’s kappa was 0.95.

Demographics of included studies

Diabetes mellitus

Seven studies were included examining DM.27–33 Three were cohort,27–29 three were case-control30–32 and three were cross-sectional.27 29 33 Taylor et al27 and Akoudad et al29 performed both cross-sectional and prospective cohort studies with their cohorts. The studies were conducted in Turkey, Taiwan and USA. They sampled varying populations, from hospital inpatients to national patient data. Patients with type 1 DM were included in all but one of the studies32 (see table 1).

Table 1

Study demographics

The male to female ratio and mean age for each study is detailed in table 1. DM and KSD ascertainment ranged from the patient reporting the diagnosis to International Classification of Diseases (ICD) codes in medical records.

Overall there were 618 120 patients, of which 28 329 (4.6%) had DM. These figures include 17 577 patients with DM in cohort studies with 348 036 controls (see table 2) and 10 752 patients with DM in case-control or cross-sectional studies with 241 755 controls (see table 3). In the cohort studies, 1312 (7.5%) of patients with DM developed KSD compared with 11 516 (3.3%) of controls. In the case-control and cross-sectional studies, 1097 (10.2%) of diabetics had KSD compared with 11 985 (5.0%) of controls. Study reported risk is detailed in tables 2 and 3.

Table 2

DM cohort studies

Table 3

DM and IGT case-control and cross-sectional studies

Metabolic syndrome

There were six studies34–39 examining metabolic syndrome, of which five provided data on chronic hyperglycaemia (IGT/DM).34–37 39 All of these studies were cross-sectional. These took place in Italy, South Korea, Taiwan and USA. The samples ranged from hospital inpatients to representative population-based studies, which were representative of target populations (see table 1).

The male to female ratio and mean age for each study is detailed in table 1. MetS and KSD ascertainment ranged from the patient-reported diagnosis to ICD codes in medical records.

Overall there were 209 817 patients, of whom 31 767 (17.8%) had MetS, 12 770 (6.1%) had IGT only (see table 4); 2258 (7.1%) of those with MetS had KSD, compared with 7593 (4.3%) of controls and 387 (3.2%) of those with IGT had KSD, compared with 1009 (1.9%) of controls (see table 3). Unfortunately control population had to be calculated from the OR for some of the studies,34–36 therefore the figures for IGT are estimates. Study reported risk is detailed in tables 3 and 4.

Table 4

MetS cross-sectional studies


Tests for overall unadjusted effect in those with DM demonstrated significantly higher risk of KSD (RR=1.66 (95% CI: 1.27 to 2.18, p<0.001). Subgroup analyses by study type demonstrated significantly higher risk of KSD in patients with DM in cohort studies in both unadjusted (1.36, 95% CI: 1.11 to 1.60, p<0.001) (see figure 1) and adjusted risk (RR=1.23, 95% CI: 0.94 to 1.51, p<0.001) (see figure 2). Significantly increased risk was also demonstrated in cross-sectional/case-control studies in both unadjusted (OR=1.49, 95% CI: 1.09 to 1.89, p<0.0001) and adjusted risk (OR=1.32, 95% CI: 1.21 to 1.43, p<0.001) (see figure 3). IGT in the context of MetS demonstrated significantly increased risk in both unadjusted (OR=1.25, 95% CI: 1.16 to 1.54, p<0.0001) and adjusted risk (OR=1.26, 95% CI: 0.94 to 1.58) [see figure 3]. Combining DM case-control and cross-sectional studies with IGT demonstrated significantly increased risk in both unadjusted (OR=1.38, 95% CI: 1.18 to 1.59, p<0.0001) and adjusted risk (OR=1.32, 95% CI: 1.17 to 1.49, p<0.0001).

Figure 2

Forest plot analysis – diabetes mellitus cohort. NHS, NationalHealth Service; RR, risk ratio.

Figure 3

Forest plot analysis – diabetes mellitus + impaired glucose tolerance cross-sectional and case-control studies. NHS, National Health Service.

Cross-sectional studies examining MetS also demonstrated significantly increased risk of KSD in both unadjusted (OR=1.74, 95% CI: 1.45 to 2.04, p<0.0001) and adjusted (OR=1.35, 95% CI: 1.16 to 1.54, p<0.0001) (see figure 4) values.

Figure 4

Forest plot analysis – metabolic syndrome (cross-sectional).

Heterogeneity and sensitivity analysis

There was borderline significant statistical heterogeneity between DM cohort studies in unadjusted risk (Tau2=0.042, Cochran’s Q=9.50, p=0.05, I2=62.3%), however there was significant heterogeneity when risk was adjusted (Tau2=0.070, Cochran’s Q=13.70, p=0.008, I2=80.2%).

There was significant statistical heterogeneity between DM case-control/cross-sectional studies in unadjusted risk (Tau2=0.258, Cochran’s Q=104.67, p<0.0001, I2=93.2%), however this was non-significant for adjusted risk (Tau2=0.00, Cochran’s Q=6.46, p=0.26, I2=0.0%).

There was non-significant statistical heterogeneity between IGT cross-sectional studies for unadjusted risk (Tau2=0.003, Cochran’s Q=7.18, p=0.30, I2=21.6%), however this was significant for adjusted risk (Tau2=0.086, Cochran’s Q=62.21, p<0.0001, I2=92.7%).

Combination of cross-sectional IGT studies with cross-sectional/case-control DM studies demonstrated significant heterogeneity for both unadjusted (Tau2=0.11, Cochran’s Q=160.10, p<0.0001, I2=91.2%) and adjusted risk (Tau2=0.044, Cochran’s Q=75.4, p<0.001, I2=81.2%). However, there was no statistical difference between subgroups for either unadjusted (I2=0%, p=0.54) or adjusted risk (I2=0%, p=0.60).

There was significant statistical heterogeneity between MetS cross-sectional studies for both unadjusted risk (Tau2=0.092, Cochran’s Q=26.08, p<0.0001, I2=79.5%), and adjusted risk (Tau2=0.034, Cochran’s Q=22.71, p<0.001, I2=72.7%).

Publication bias and quality of evidence

Leave-one-out analysis did not identify any studies that significantly changed the RR or OR for DM with and without IGT inclusion, nor for MetS.

Trim and fill analysis did no demonstrate any missing studies for DM without IGT (SE=2.21). Inclusion of IGT with DM demonstrated six missing studies (SE=2.75) (see figure 5). The analysis demonstrated lack of negative studies. Trim and fill analysis of MetS demonstrated two missing studies (SE=1.78) (see figure 6), both negative.

Figure 5

Funnel plot - diabetes mellitus with impairedglucose tolerance. Black dots=included studies, white dots=missing studies identified on ‘trim and fill analysis’.

Figure 6

Funnel plot - metabolic syndrome. Black dots=included studies, white dots=missing studies identified on ‘trim and fill analysis’.

Egger’s regression demonstrated no significant results for: DM without IGT (z=0.81, p=0.42), DM with IGT (z=0.85, p=0.40) or MetS (z=0.15, p=0.88).

Overall there was a moderate risk of bias. All but two studies27 28 had scores greater than 7 on examination with the Newcastle-Ottawa quality assessment scale (see tables 5–7). Broadly taking in all studies there were no sample size calculations or demonstrable levels of response. None of the cohort studies provided Consolidated Standards of Reporting Trials diagrams nor did they provide loss to follow-up data in the text.

Table 5

Bias analysis of cohort studies

Table 6

Bias analysis of cross-sectional studies

Table 7

Bias analysis of case-control studies


In this review and meta-analysis DM carried a significantly increased risk of developing KSD in cohort studies with a low risk of bias. Cross-sectional and case-control studies also demonstrate significantly increased likelihood of having KSD in those who have DM with a moderate risk of bias. IGT in the context of MetS carries a similar likelihood to DM in cross-sectional studies.

MetS carries a similar likelihood to DM and IGT in the context of MetS, with little difference between each in terms of adjusted ORs, again with a moderate risk of bias.

This is the first systematic review and meta-analysis to examine DM and MetS together. The results are highly significant although are limited by heterogeneity, despite meta-regression analysis. The results for DM are likely to be reflective of the true situation given that there were no missing studies identified on ‘trim and fill’ analysis. The situation for IGT and MetS may not be reflective given some negative studies were identified, and therefore there is a risk of publication bias.

The main strength in this study is the cohort studies examining DM, which have long follow-up periods and demonstrate highly significant results with a low risk of bias, despite suffering from significant statistical heterogeneity. This may be the result of differing adjustments between studies.

The case-control and cross-sectional studies examining DM were of variable quality but demonstrated highly significant results, similar to the cohort studies. Direct comparison between cohorts and these studies is difficult due to the differing outcome measure

There was no differentiation between type 1 and type 2 DM in most studies. It is unclear if type 1 confers the same risk as type 2.

It was unclear from the studies whether IGT was considered in isolation or in combination with other MetS components, nor was it clear whether the comparator groups contained those with MetS components, without reaching the required three components needed for diagnosis. This risks falsely lowering the risk associated with IGT due to the comparisons with other potential KSD risk factors.

Statistical heterogeneity demonstrated in most of the analyses may be due to ascertainment of KSD, variability in study populations and design and publication bias. There were significant variations in KSD ascertainment from patient-reported to medical notes to radiologically proven. Some studies may therefore under-report the true number of stones.

Variability in study populations and design (cohort, cross-sectional and case-control) ranged from hospital attendees in a single centre to large regional or national cohort studies. The effect of this variability is somewhat negated by dividing the studies by study design and analysing each separately.

DM cohort study adjusted values although the overall figure was significant the CI includes 1, therefore this could represent type 1 error.

Publication bias was low in this study with trim and fill analyses demonstrating few missing studies (mostly for MetS) and leave-one-out analysis not demonstrating any significantly heterogeneous studies.

The most common stone composition in all KSD formers is calcium oxalate, followed closely by calcium phosphate, together comprising around 85% of all stones. Uric acid stones are third, accounting for 12% in men, 7% in women, while the far rare cystine stones account for less than 1% in either gender.40 Both DM and MetS have been linked to increased uric acid stone formation, while calcium stone formation remains static, seemingly un-influenced by either DM or MetS.41

The increased risk of KSD in DM is thought to be secondary to two factors, glycaemic control (common to both types 1 and 2 and impaired glucose tolerance) and insulin resistance (as seen in type 2 DM and MetS). Hyperglycaemia has been demonstrated to increase urinary calcium,42 43 phosphorous,42 43 uric acid44 45 and oxalate46 secretion. Whereas increased insulin resistance increases renal ammonium secretion47 and decreased urinary pH,46 which in turn increases urinary calcium and uric acid secretion48 while decreases urinary citrate49 (an alkalizing agent), compounding urinary acidification. Together these mechanisms lead to increased risk of precipitation and subsequent formation of uric acid stones.

Notably, Chung et al50 and Weikert51 in prospective cohort studies demonstrated patients who suffered from KSD were more likely to develop DM over a 5-year period than those who did not form stones. This muddies the water, giving a ‘chicken and egg’ scenario. It could be that KSD is a symptom of an underlying systemic metabolic disorder, or something intrinsic to KSD formers increases the risk of metabolic derangement. The former is more likely given the evidence for biochemical disruption in urinary excretions prior to stone formation.

Metabolic syndrome has been defined multiple times,52 however all definitions are in agreement that it comprises a combination of insulin resistance, hypertension and dyslipidaemia. Insulin resistance in metabolic syndrome is the same mechanism resulting in type 2 diabetes and thus the findings of urinary acidification,48 53 increased risk of uric acid secretion53 and uric acid stone formation48 via the pathophysiology described above are the same.

In this review a small, although non-significant increase in risk suffering from heterogeneity, was associated with MetS versus IGT/DM. This may be attributable to the other components of MetS.

There is conflicting evidence about hypertension and a possible link to increased risk of KSD35 and vice versa.54 A prospective cohort study by Cappuccio et al55 demonstrated a significantly increased crude risk of hypertensives developing KSD than non-hypertensives. However, when observing the difference between stone formers and non-stone formers, the stone formers had no significant difference in blood pressure. It was noted that the hypertensives were significantly heavier, older and had higher BMI’s. Madore et al in consecutive studies on both genders,54 56 demonstrated there was no increased risk compared with non-hypertensive individuals when age, BMI and electrolyte intake were adjusted for. Akoudad et al27 in their prospective cohort study demonstrated an increased risk of KSD with hypertension. However on multivariate analysis the effect was rendered non-significant. Perhaps the risk found by Cappuccio was confounded by the presence of metabolic syndrome, which at the time of publication was not defined.20 Hypertension is more likely indicative of underlying metabolic disturbance than having a truly lithogenic effect.

Dyslipidaemia, defined as hypercholesterolaemia, low serum high-density lipoprotein and high serum triglycerides20 has also been associated with increased risk of KSD.57 However, when adjusted in multivariate analysis the association is lost.57 Moreover, the only demonstrable biochemical abnormality after multivariate analysis is high urinary uric acid. Therefore the risk associated with dyslipidaemia is due to insulin resistance instead.

Renal lipotoxicity, defined as lipid accumulation in non-adipose tissues, has been linked to decreased ammonium secretion and therefore lower pH in rat models.58 However, this observation has yet to be reflected in humans. Renal lipotoxicity may represent the endpoint of chronic dyslipidaemia.

The addition of renal lipotoxicity to insulin resistance may explain the seemingly increased risk of KSD observed in patients with MetS versus IGT. Further studies are required to accurately demonstrate the underlying mechanism.

The rise in prevalence of DM and MetS is well documented and is now perceived as a global pandemic.9 18 KSD prevalence has risen in parallel.3 5 6 The Global Burden of Disease study9 10 demonstrated morbidity and absolute mortality associated with KSD has increased, perhaps due to the pandemic of DM/MetS,19 although age standardised mortality rates have decreased globally,. The effect is marked in higher income countries, but is attenuated in lower-middle income countries.8 10 This may be attributable to lack of availability of prompt intervention in developing countries, leading to later presentation and invasive treatments including nephrectomy.59–61 Following surgical treatment, management to prevent recurrence is recommended,13 again this may not be available in developing countries.

In this review, those with impaired glucose tolerance (pre-diabetes) had an increased likelihood of KSD, which was similar to those with DM in cross-sectional/case-control studies, although this may be suffering from publication bias and the real situation may be that the likelihood of KSD in IGT is lower than DM. Indeed, The NationalHealth and Nutrition Examination Survey III cross-sectional study33 demonstrated with increasingly poor glycaemic control led to increasing likelihood of KSD as determined by fasting plasma glucose and glycosylated haemoglobin. Given the evidence suggesting those with DM or MetS are at increased risk of developing KSD measures to improve glycaemic control should be examined for their efficacy in KSD prevention in this ‘at-risk’ population. It should be noted that the stone type in those with DM or MetS is most commonly calcium oxalate, however although still small, the proportion of urate stones increases in these related populations.22 62

Clarity is required on the risk in type 1 diabetics and future studies should differentiate these patients from type 2. Further prospective examination of DM and MetS should be undertaken to accurately portray whether additional risk is posed by MetS over DM and quantify this. Tight glycaemic control and weight loss should be explored in primary prevention studies for both MetS and DM, given the common pathophysiological mechanism. Further investigation is required to demonstrate if these patient are at increased risk of recurrence.

The risk of developing kidney stones is significantly increased in populations with chronic hyperglycaemia. This has global implications with rising morbidity and absolute mortality attributable to stones and is likely to increase the health and economic burden on patients and healthcare providers. Tight glycaemic control and weight loss are low-cost and non-invasive measures, which should be investigated for their primary preventative effect on KSD in these populations and included as part of the long-term management of kidney stone disease.



  • Twitter @RobertGeraght16, @endouro

  • Contributors RG performed the search, statistical analysis and wrote the manuscript. AA performed the search and reviewed the manuscript. PC, BS and PR edited the manuscript and critiqued the statistical analysis. BS and PR decided whether or not to include studies as the senior authors.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available in a public, open access repository. Data are available upon reasonable request.