M Abouleka

F Hallot-Feron

K Fumeron

S Mohammedi

R Hadjadj

G Roussel

M Velho

Marre

Odette Matar

Louis Potier

Yawa Abouleka

Marilyne Hallot-Feron

Frédéric Fumeron

Kamel Mohammedi

Samy Hadjadj

Ronan Roussel

Gilberto Velho

Professeur Michel Marre
email: marre.michel@gmail.com

Relationship between renal capacity to reabsorb glucose and renal status in patients with diabetes

Keywords: Diabetic kidney disease, Renal glucose transport, Tubuloglomerular feedback

diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Hyperglycaemia is associated with alterations in the tubuloglomerular feedback system leading to an increase in glomerular filtration rate (GFR) [START_REF] Blantz | Effect of modest hyperglycemia on tubuloglomerular feedback activity[END_REF]. Such an effect results from the inappropriate openingof preglomerular vascular resistances, mediated by hyperglycaemiarelated changes in the composition of tubular fluid and altered sensing of sodium load by the macula densa [START_REF] Vallon | Salt-sensitivity of proximal reabsorption alters macula densa salt and explains the paradoxical effect of dietary salt on glomerular filtration rate in diabetes mellitus[END_REF]. This opening of preglomerular resistances causes hypertension in the glomerular circulation and itsrelated renovascular complications [START_REF] Brenner | The hyperfiltration theory: a paradigm shift in nephrology[END_REF].

It has previously been reported that increased renal reabsorption of glucose during hyperglycaemia, coupled with increased reabsorption of sodium in the proximal tubule, is a characteristic of patients with type 1 diabetes (T1D) with glomerular hyperfiltration and/or microalbuminuria, two established risk factors of chronic kidney disease (CKD) [START_REF] Carpentier | Glycosuria amount in response to hyperglycaemia and risk for diabetic kidney disease and related events in type 1 diabetic patients[END_REF].

Additional evidence of a possible link between increased renal glucose reabsorption and kidney disease was also suggested by the results of recent clinical trials of sodium-glucose cotransporter 2 (SGLT2) inhibitors. Indeed, these molecules, which decrease glucose and sodium reabsorption in the proximal tubule, have shown beneficial effects on kidney outcomes in high-risk patients with type 2 diabetes (T2D) [START_REF] Wanner | Empagliflozin and progression of kidney disease in type 2 diabetes[END_REF][START_REF] Neal | Canagliflozin and cardiovascular and renal events in type 2 diabetes[END_REF][START_REF] Wiviott | Dapagliflozin and cardiovascular outcomes in type 2 diabetes[END_REF].

The risk of kidney disease is highly variable among individuals with diabetes. Allelic variations in components of the renin-angiotensin system [START_REF] Smyth | Genetic associations between genes in the renin-angiotensin-aldosterone system and renal disease: a systematic review and meta-analysis[END_REF], notably the I/D polymorphism in the ACE gene [START_REF] Marre | Contribution of genetic polymorphism in the renin-angiotensin system to the development of renal complications in insulin-dependent diabetes: Genetique de la Nephropathie Diabetique (GENEDIAB) study group[END_REF], confer a major increase in risk, although many other genetic markers and biomarkers of diabetic kidney disease (DKD) have been identified [START_REF] Mohammedi | Catalase activity, allelic variations in the catalase gene and risk of kidney complications in patients with type 1 diabetes[END_REF][START_REF] Velho | Plasma copeptin and renal outcomes in patients with type 2 diabetes and albuminuria[END_REF][START_REF] Patente | Allelic variations in the CYBA gene of NADPH oxidase and risk of kidney complications in patients with type 1 diabetes[END_REF][START_REF] Velho | Plasma adrenomedullin, allelic variation in the ADM gene and kidney disease in people with type 2 diabetes[END_REF][START_REF] Velho | Plasma copeptin, kidney outcomes, ischemic heart disease, and all-cause mortality in people with long-standing type 1 diabetes[END_REF][START_REF] Sandholm | The genetic landscape of renal complications in type 1 diabetes[END_REF][START_REF] Sanchez | Plasma concentrations of 8-hydroxy-2'-deoxyguanosine and risk of kidney disease and death in individuals with type 1 diabetes[END_REF][START_REF] Van Zuydam | A genome-wide association study of diabetic kidney disease in subjects with type 2 diabetes[END_REF][START_REF] Skupien | Variations in risk of end-stage renal disease and risk of mortality in an international study of patients with type 1 diabetes and advanced nephropathy[END_REF]. The maximum capacity for glucose reabsorption across the proximal tubule, the primum movensof impaired tubuloglomerular feedback in diabetes [START_REF] Vallon | Salt-sensitivity of proximal reabsorption alters macula densa salt and explains the paradoxical effect of dietary salt on glomerular filtration rate in diabetes mellitus[END_REF][START_REF] Cherney | Renal hemodynamic effect of sodium-glucose cotransporter 2 inhibition in patients with type 1 diabetes mellitus[END_REF], is also a variable trait [START_REF] Bonnardeaux | Inherited disorders of the renal tubule[END_REF]. Thus, our hypothesis was that interindividual variability in the capacity to reabsorb glucose at the proximal tubule could contribute to risk of DKD. For this reason, our present study investigated, in patients with diabetes, the association of fractional reabsorption of glucose (FRGLU), a marker of tubular capacity to reabsorb glucose independent of GFR, with degree of renal disease, as assessed by urinary albumin excretion (UAE) and estimated GFR (eGFR).

Materials and Methods

From September 2017 to January 2018, all patients with diabetes attending the Diabetes Department at the Bichat-Claude Bernard University Medical Centre in Paris, France, for a one-day global assessment of their condition, or for hospitalization in our ward, were asked to participate to the present study. All underwent, as part of their routine evaluation, blood and urine sampling in a fasting state. Plasma, urinary glucose and creatinine, glycated haemoglobin (HbA1c) and urinary albumin were measured, and age, gender, diabetes type and duration, and antidiabetic and other treatments were also recorded. No patients were taking SGLT2 inhibitors, which were not available in France at the time. Glucose and creatinine were measured by enzymatic methods, HbA1c by high-performance liquid chromatography (HPLC) and urinary albumin by nephelometry, all through the use of automated systems (Siemens AG, Munich, Germany). Patients' blood pressure (BP) was taken three times at 2-min intervals with a Dinamap BP monitor (GE Healthcare, Princeton, NJ, USA) in a sitting position, and the median value recorded.

All patients gave their informed consent to participate, and the investigation was carried out in accordance with the principles of the Declaration of Helsinki (as revised in 2008).

FRGLU was calculated as 1 minus fractional glucose clearance, which was defined as urinary glucose clearance divided by GFR. Urinary glucose clearance was defined as urinary glucose concentration multiplied by urine volume divided by plasma glucose. Creatinine clearance was used as an estimate of GFR: this was defined as the urinary creatinine concentration multiplied by urine volume divided by plasma creatinine. Thus, fractional glucose clearance was calculated as (urinary glucose × plasma creatinine)/(urinary creatinine × plasma glucose) and the FRGLU expressed as a percentage.

Because the FRGLU is primarily dependent on the prevailing blood glucose level, participants were stratified according to their admission fasting plasma glucose (FPG) levels as low (< 6 mmol/L), intermediate (6-11 mmol/L) and high (> 11 mmol/L). These glycaemic thresholds were chosen because 11 mmol/L is widely recognized to be the maximum threshold for glycosuria to arise [START_REF] Bonnardeaux | Inherited disorders of the renal tubule[END_REF], while < 6 mmol/L defines the upper limit of normal glucose tolerance based on FPG. The 6-11 mmol/L FPG is a zone of uncertainty for glycosuria. Patients using a flash continuous glucose monitoring system (FreeStyle Libre, Abbott Laboratories, Abbott Park, IL, USA) were asked to report their average blood glucose during the 8 h preceding blood sampling. This allowed confirmation of the representativeness of the blood glucose levels-the average blood glucose during the hours prior to urine collection-used to calculate the FRGLU.

Classification of CKD was based on the UAE and expressed as the albumin/creatinine ratio (ACR), while eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [START_REF] Levey | A new equation to estimate glomerular filtration rate[END_REF]. UAE was categorized as normoalbuminuria (ACR < 30 mg/g), microalbuminuria (ACR 30-300 mg/g) and macroalbuminuria (ACR > 300 mg/g); eGFR was categorized according to the Kidney Disease: Improving Global Outcomes (KDIGO) classification [START_REF]Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease[END_REF] as G1 (> 90 mL/min/1.73 m 2 ), G2 (90-60 mL/min/1.73 m 2 ), G3 (59-30 mL/min/1.73 m 2 ) and G4 (< 30-16 mL/min/1.73 m 2 ). Patients with eGFR ≤ 15 mL/min/1.73 m 2 (G5) were excluded from the study. UAE and eGFR contribute independently and additively to risk for end-stage kidney disease in diabetes [START_REF] Ninomiya | Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes[END_REF]. Thus, for a sensitivity analysis, a score of kidney disease severity (ranked 0 to 5) was calculated for each participant by summing UAE and eGFR scores: normoalbuminuria (0), microalbuminuria (1) and macroalbuminuria (2), and G1 (0), G2 (1), G3 (2) and G4 (3), respectively. Data are presented as means (standard deviation, SD) and medians (interquartile range, IQR), and as means (standard error of mean, SEM) for analysis of covariance (ANCOVA) results.

As FRGLU values were not normally distributed but significantly skewed towards the higher end of the distribution, these values were ranked and the ranking order used for all statistical analyses. Comparisons of FRGLU ranking between groups were made by Kruskal-Wallis tests, and Cochran-Mantel-Haenszel tests were used to assess group interactions. Correlations between FRGLU ranking and kidney disease severity scores were assessed by Spearman's rank correlation coefficient (rho). Comparisons of other traits between groups were made by ANCOVA, Kruskal-Wallis or Pearson's chi-squared tests. Assessments of the covariates associated with UAE and eGFR were made using multiple regression analyses. Statistical analyses were performed with JMP software (SAS Institute Inc., Cary, NC, USA), and P ≤ 0.05 was considered significant.

Results

The present study analyzed data from 637 of the 651 participants after excluding patients who had eGFRs ≤ 15 mL/min/1.73 m 2 . Thus, the study population consisted of 62 patients with T1D, 482 with T2D and 93 with some other type of diabetes (62 had diabetes and pancreatic disease, 31 had monogenic diabetes). Clinical characteristics of participants stratified by FPG group at admission are presented in Table I, and treatment data are shown in Table S1 (see supplementary materials associated with this material online). Among the 213 participants using flash glucose monitoring devices, admission FPG correlated well with the average blood glucose recorded by their devices during the 8 h preceding blood samplings (r 2 = 0.642, P < 0.0001). Patients in the high FPG group compared with the other groups more frequently had T1D, higher HbA1c levels and lower FRGLU values, whereas eGFR did not differ by FPG group (Table I).

Characteristics of our study patients stratified by UAE categories are presented in Table II, and FRGLU by UAE categories in the three FPG groups are shown in Fig. 1. There was a significant interaction between UAE category and FPG group in the FRGLU distribution (P < 0.0001). Whereas FRGLU was not affected by UAE category in either the low FPG (P = 0.14) or intermediate FPG (P = 0.16) groups, in the high FPG group, median (IQR) FRGLU values increased from 93.50% (17.74) in patients with normoalbuminuria (n = 135) to 96.56% (5.94) in those with microalbuminuria (n = 77) and to 99.12% (5.44) in those with macroalbuminuria (n = 31; P = 0.002). Fig. 3A shows glycosuria plotted by FRGLU rank in participants with high FPG stratified by UAE category: glycosuria was 137 ± 8 mmol/L, 114 ± 10 mmol/L and 65 ± 16 mmol/L in patients with normoalbuminuria, microalbuminuria and macroalbuminuria, respectively (presented as means ± SEM; P < 0.0001; ANCOVA adjusted for FRGLU rank and FPG).

Characteristics of patients stratified by eGFR category are presented in Table III: HbA1c and FPG (or FPG groups) did not differ by eGFR category. FRGLU by eGFR category in the three FPG classes are shown in Fig. 2. There was a significant interaction between eGFR category and FPG group on FRGLU distribution (P < 0.0001): FRGLU increased across eGFR categories in those with high FPG: median (IQR) FRGLU values were 94.13% (16.24) for G1 (n = 111), 96.35% (11.94) for G2 (n = 72), 98.88% (7.59) for G3 (n = 46) and 99.11% (2.18) for G4 (n = 14; P = 0.002). Fig. 3B shows glycosuria plotted by FRGLU rank in participants with high FPG stratified by eGFR category: glycosuria means ± SEM were 154 ± 8 mmol/L, 109 ± 10 mmol/L, 79 ± 13 mmol/L and 40 ± 23 mmol/L in G1, G2, G3 and G4 patients, respectively (P < 0.0001; ANCOVA adjusted for FRGLU rank and FPG).

The results of multiple regression analysis of covariates associated with UAE and eGFR in those with high FPG are shown in Table IV. FRGLU was independently associated with UAE (P = 0.003) and eGFR (P = 0.04) in a regression model including age, gender, duration and type of diabetes, systolic BP, HbA1c and use of angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs). Type of diabetes was not significantly associated with either UAE or eGFR in these analyses.

Sensitivity analyses were performed with renal disease scores that took into account both UAE and eGFR categories. The results revealed a significant correlation between renal disease score and FRGLU: Spearman's rho = 0.08 (P = 0.04) for FPG groups combined; and rho = 0.29 (P < 0.0001) for the high FPG group on its own.

Discussion

In this cross-sectional study of patients with diabetes, their renal capacity to reabsorb glucose (FRGLU) during hyperglycaemia (FPG > 11 mmol/L) was indeed associated with kidney disease. In fact, FRGLU was higher in patients with more severely impaired kidney function in terms of UAE, eGFR or a kidney disease score of these two markers combined. On the other hand, glycosuria was lower in patients with more severely impaired kidney function. These results were unexpected, as tubules are reportedly anatomically damaged in kidney biopsies of patients with established diabetic nephropathy [START_REF] Najafian | Pathology of human diabetic nephropathy[END_REF]. Thus, intuitively, it would be expected that a reduction in glucose reabsorption, a tubular function, would be observed in those with reduced kidney function. One possible explanation is that the tubular capacity to transport glucose from the urine back to the capillary bloodstream was not saturared in those with low and intermediate FPG, not even in participants with reduced kidney function. Also, hyperglycaemia stimulates tubular reabsorption capacity to reabsorb glucose [START_REF] Wright | Biology of human sodium glucose transporters[END_REF]. As the most consistent associations between UAE and eGFR with FRGLU were observed in the group of patients with high FPG (> 11 mmol/L), this suggests that maximum glucose reabsorption activity had been reached in those patients.

The major limitation of our present study was that it was a cross-sectional single-centre investigation and a reverse causation relationship cannot be excluded. Therefore, our study can only bring first-level evidence to our hypothesis, and further investigations are now required for confirmation of our findings. In addition, as the distribution of FRGLU was far from normal, only non-parametric tests were used in this study, and whether the statistical significance levels reported here are clinically meaningful or not still needs to be ascertained. Thus, it is important to design follow-up studies to investigate the renal outcomes of patients selected according to their baseline glycaemic control.

The major strength of our study was that it evaluated a large sample of patients with several types of diabetes, and variable degrees of diabetes control and duration.

The pathophysiological mechanisms related to renal capacity to reabsorb glucose and risk for kidney disease have yet to be clearly determined. They could be related, at least in part, to changes in sodium concentrations in tubular fluid. Tubular reabsorption of glucose by SGLT1 and SGLT2 is coupled with sodium reabsorption, and increased tubular reabsorption of sodium leads to low tubular lumen concentrations of NaCl and reduced NaCl uptake at the macula densa, thereby triggering the complex mechanisms of tubuloglomerular feedback leading to increased renin release and activation of the renin-angiotensin system.

Previously, increased reabsorption of both glucose and sodium at the proximal tubule level, and increased GFR following the transition from near-normal glycaemia to hyperglycaemia, have been reported during intravenous glucose infusion in patients with T1D and glomerular hyperfiltration and/or microalbuminuria [START_REF] Carpentier | Glycosuria amount in response to hyperglycaemia and risk for diabetic kidney disease and related events in type 1 diabetic patients[END_REF]. These effects were largely attenuated in patients with normal GFR and UAE. Over the 20-year follow-up, a high renal capacity to reabsorb glucose and sodium contributed independently to other known risk factors for the development of kidney disease [START_REF] Carpentier | Glycosuria amount in response to hyperglycaemia and risk for diabetic kidney disease and related events in type 1 diabetic patients[END_REF]. Moreover, clinical trials of SGLT2 inhibitors, which decrease tubular reabsorption of glucose, have shown beneficial effects on kidney outcomes in both T1D and T2D patients [START_REF] Wanner | Empagliflozin and progression of kidney disease in type 2 diabetes[END_REF][START_REF] Neal | Canagliflozin and cardiovascular and renal events in type 2 diabetes[END_REF][START_REF] Wiviott | Dapagliflozin and cardiovascular outcomes in type 2 diabetes[END_REF][START_REF] Cherney | Renal hemodynamic effect of sodium-glucose cotransporter 2 inhibition in patients with type 1 diabetes mellitus[END_REF], including correction of glomerular hyperfiltration during hyperglycaemia in those with recent-onset T1D [START_REF] Cherney | Renal hemodynamic effect of sodium-glucose cotransporter 2 inhibition in patients with type 1 diabetes mellitus[END_REF]. Our present findings are consistent with the results of those investigations [START_REF] Carpentier | Glycosuria amount in response to hyperglycaemia and risk for diabetic kidney disease and related events in type 1 diabetic patients[END_REF][START_REF] Wanner | Empagliflozin and progression of kidney disease in type 2 diabetes[END_REF][START_REF] Neal | Canagliflozin and cardiovascular and renal events in type 2 diabetes[END_REF][START_REF] Wiviott | Dapagliflozin and cardiovascular outcomes in type 2 diabetes[END_REF][START_REF] Cherney | Renal hemodynamic effect of sodium-glucose cotransporter 2 inhibition in patients with type 1 diabetes mellitus[END_REF].

The basis for interindividual variability in renal glucose reabsorption capacity is heterogeneous and probably includes variations in the genes implicated in tubular glucose handling, including its energy demands. Mutations in SLC5A2, the gene encoding SGLT2, result in familial renal glycosuria [START_REF] Santer | Molecular analysis of the SGLT2 gene in patients with renal glucosuria[END_REF]. Mutations in transcription factor HNF-1α, a protein that binds to the SLC5A2 promoter and regulates SGLT2 expression, decrease tubular glucose reabsorption and lead to renal glycosuria [START_REF] Pontoglio | HNF-1a controls renal glucose reabsorption in mouse and man[END_REF]. SLC5A2 and HNF1A gene polymorphisms are frequent in the general population, yet their impact on glucose transport, renal function and kidney outcomes still needs to be established. Proximal tubules reabsorb 80% of the filtrate that passes through the glomerulus, and their reabsorption capacity depends on the efficiency of oxidative phosphorylation to produce adenosine triphosphate (ATP), which drives the active transport of glucose, ions and nutrients. As such, these tubules contain more mitochondria than any other structure in the kidney [START_REF] Bhargava | Mitochondrial energetics in the kidney[END_REF]. Renal mitochondria are sensitive to oxidative stress, and mitochondrial fragmentation and changes in mitochondrial energetics have been observed in proximal tubules in the early stages of diabetes [START_REF] Coughlan | Mapping time-course mitochondrial adaptations in the kidney in experimental diabetes[END_REF]. Indeed, there have been reports of the association of allelic variations in genes implicated in oxidative stress or in antioxidant mechanisms with the development and progression of DKD in T1D [START_REF] Mohammedi | Catalase activity, allelic variations in the catalase gene and risk of kidney complications in patients with type 1 diabetes[END_REF][START_REF] Patente | Allelic variations in the CYBA gene of NADPH oxidase and risk of kidney complications in patients with type 1 diabetes[END_REF][START_REF] Mohammedi | Manganese superoxide dismutase (SOD2) polymorphisms, plasma advanced oxidation protein products (AOPP) concentration and risk of kidney complications in subjects with type 1 diabetes[END_REF][START_REF] Mohammedi | Allelic variations in superoxide dismutase-1 (SOD1) gene are associated with increased risk of diabetic nephropathy in type 1 diabetic subjects[END_REF][START_REF] Mohammedi | Glutathione peroxidase-1 gene (GPX1) variants, oxidative stress and risk of kidney complications in people with type 1 diabetes[END_REF][START_REF] Monteiro | Sex-specific associations of variants in regulatory regions of NADPH oxidase-2 (CYBB) and glutathione peroxidase 4 (GPX4) genes with kidney disease in type 1 diabetes[END_REF]. Nevertheless, the impact of these frequent variants on tubular function also needs to be established.

In conclusion, the present study suggests that maximum glucose reabsorption capacity can influence the risk of kidney disease in patients with diabetes and that interindividual heterogeneity of this function is a determinant of heterogeneity in the risk for diabetic kidney disease. However, further studies are now required to explore this hypothesis, as they are likely to contribute some interesting data towards the creation of personalized treatment strategies to protect diabetes patients against kidney disease.

Highlights

• Susceptibility to kidney disease is heterogeneous in patients with diabetes;

• Assessment of fractional renal glucose reabsorption in patients with diabetes revealed a strong association between glucose reabsorption capacity and renal status severity;

• High renal glucose reabsorption might be contributing to diabetic kidney disease.

Legends for figures

  Abbreviations:

Fig. 1 .

 1 Fig. 1. Fractional reabsorption of glucose (FRGLU) by urinary albumin excretion (UAE)

Fig. 2 .

 2 Fig. 2. Fractional reabsorption of glucose (FRGLU) by estimated glomerular filtration rate

Fig. 3 .

 3 Fig. 3. Glycosuria by fractional reabsorption of glucose ranking in participants with high

  Quantitative data expressed as means (SD) or *medians (IQR); analysis of covariance (ANCOVA) by *Kruskal-Wallis or Pearson's chi-squared test (qualitative data); P ≤ 0.05 considered significant; FRGLU: fractional reabsorption of glucose TableIVCovariates associated with urinary albumin excretion (UAE) and estimated glomerular filtration rate (eGFR) according to multivariate regression analyses in participants with high fasting plasma glucose (FPG) levels Fractional reabsorption of glucose (FRGLU) ranking order used for computations; other quantitative data log-transformed (ln); P ≤ 0.05 considered significant; ACE: angiotensin-converting enzyme; ARBs: angiotensin receptor blockers

	Table I Table II Table III
	Characteristics of participants by fasting plasma glucose (FPG) group at admission Characteristics of participants by urinary albumin excretion (UAE) category Characteristics of participants by category of estimated glomerular filtration rate (eGFR)
								FPG group UAE category eGFR category (mL/min/1.73 m 2 )
					G1 UAE	Low Normoalbuminuria G2	Intermediate Microalbuminuria G3	High Macroalbuminuria G4 eGFR	P	P	P
	Number of patients	β estimate	(> 90) SEM	(< 6 mmol/L) 303 (90-60) P	(6-11 mmol/L) 261 (59-30) β estimate	(> 11 mmol/L) 73 (30-16) SEM P
	Number of patients Age, years Number of patients Age	-0.4204	286 0.3927		106 57 (15) 0.29	210	288 63 (14) -0.6774	110	243 62 (14) 31 0.1114 < 0.0001	< 0.0001
	Age, years Male gender, n (%) Age, years Male gender		0.2046	51 (14) 0.0935		59 (14) 166 (54.8) 0.03	65 (10)	60(14) 148 (56.7) 70 (11) -0.0577	59(15) 56 (76.7) 66 (10) 0.0265 0.03	0.88 0.002 < 0.0001
	Male gender, n (%) Diabetes type: Type 1, n (%) Male gender, n (%) Type 1 diabetes	-0.1875	153 (53.5) 0.2193		65 (61.3) 36 (11.9) 135 (64.3) 0.39	156 (54.2) 20 (7.7) 60 (54.6) 0.0025	149 (61.3) 6 (8.2) 22 (71.0) 0.0622 0.97	0.19 0.04 0.17
	Diabetes type: Type 1, n (%) Type 2, n (%) Diabetes type: Type 1, n (%) Type 2 diabetes	-0.0883	44 (15.4) 0.1721		12 (11.3) 217 (71.6) 11 (5.2) 0.61	18 (6.2) 205 (78.5) 5 (4.5) 0.0317	32 (13.2) 60 (82.2) 2 (6.4) 0.0488 0.52	< 0.0001 < 0.0001
	Type 2, n (%) Other, n (%) Type 2, n (%) Diabetes duration		0.2964	192 (67.1) 0.1106		64 (60.4) 50 (16.5) 178 (84.8) 0.008	232 (83.0) 36 (13.8) 86 (78.2) -0.0197	179 (73.6) 7 (9.6) 26 (88.9) 0.0314 0.53
	Other, n (%) Diabetes duration, years* Other, n (%) HbA1c		0.3060	50 (17.5) 0.5042		30 (28.3) 11 (15) 0.54 21 (10.0)	31 (10.8) 13 (13) 19 (17.3) 0.1749	32 (13.2) 18 (12) 3 (9.7) 0.1431 0.22	< 0.0001
	Diabetes duration, years* Fasting plasma glucose (FPG), mmol/L Diabetes duration, years* Systolic blood pressure	1.8001	10 (13) 0.6725		12 (14) 11.5 (6.1) 0.008 14 (14)	12 (14) 10.1 (5.3) 18 (17) -0.2385	15 (16) 11.1 (5.4) 19 (15-27) 0.1908 0.21	0.08 < 0.0001 0.01
	FPG, mmol/L FPG group: Low (< 6), n (%) Fasting plasma glucose (FPG), mmol/L Body mass index 0.8807	10.9 (5.5) 0.4693		4.7 (1.0) 47 (15.5) 10.6 (5.6) 0.06	8.3 (1.4) 49 (18.8) 11.2 (6.7) -0.3419	16.7 (5.0) 10 (13.7) 11.1 (5.2) 0.1332 0.01	< 0.0001 0.006 0.74
	HbA1c, mmol/mol Intermediate (6-11), n (%) FPG group: Low (< 6), n (%) Use of ACE inhibitors or ARBs	0.1810	44 (15.4) 0.1133		60 (16) 121 (39.9) 33 (15.7) 0.11	67 (14) 135 (51.7) 24 (21.8) 0.0465	89 (20) 32 (43.8) 5 (16.1) 0.0321 0.15	< 0.001 0.32
	HbA1c, % FRGLU	High (> 11), n (%) Intermediate (6-11), n (%) 0.0048	131 (45.8) 0.0016		7.6 (1.9) 135 (44.6) 105 (50.0) 0.003	8.3 (1.6) 77 (29.5) 40 (36.4) -0.0009	10.3 (2.3) 31 (42.5) 12 (38.7) 0.0004 0.04	< 0.0001
	Body mass index, kg/m 2 HbA1c, mmol/mol High (> 11), n (%)			111 (38.8)		28.6 (6.3) 78 (21) 72 (34.3)	29.5 (6.1) 72 (17) 46 (41.8)	29.0 (6.7) 70 (15) 14 (45.2)	0.23 0.007
	Systolic blood pressure, mmHg HbA1c, % HbA1c, mmol/mol			76 (21)		126 (16) 9.3 (2.5)		73 (18)	129 (18) 8.7 (2.0) 73 (19)	132 (20) 8.6 (1.7) 73 (12)	0.07 0.007 0.36
	Diastolic blood pressure, mmHg Body mass index, kg/m 2 HbA1c, %			9.1 (2.4)		72 (12) 28.6 (6.3) 8.8 (2.1)	72 (12) 29.4 (6.2) 8.8 (2.2)	73 (13) 31.0 (6.5) 8.5 (1.4)	0.76 0.36 0.02
	Urinary albumin excretion (UAE), mg/g* Systolic blood pressure, mmHg Body mass index, kg/m 2	28.2 (6.2)		24 (43) 127 (18) 29.9 (6.5)	25 (55) 132 (18) 29.5 (5.6)	16 (50) 137 (20) 32.3 (7.4)	0.15 < 0.0001 0.001
	UAE category: Normoalbuminuria, n (%) Diastolic blood pressure, mmHg Systolic blood pressure, mmHg	126 (17)		47 (44.3) 71 (12)	133 (19)	121 (42.0) 74 (12) 132 (18)	135 (55.5) 73 (11) 141 (20)	0.006 0.02 < 0.0001
	Microalbuminuria, n (%) Urinary albumin excretion, mg/g* Diastolic blood pressure, mmHg	74 (12)		49 (46.2) 9 (7)		73 (12)	135 (46.9) 42 (50) 69 (12)	77 (31.7) 580 (917) 71 (12)	< 0.0001 0.02
	Macroalbuminuria, n (%) Estimated glomerular filtration rate (eGFR), mL/min/1.73 m 2 Urinary albumin excretion (UAE), mg/g* 14 (28)		10 (9.5) 91 (27)		21 (42)	32 (11.1) 81 (27) 50 (176)	31 (12.8) 57 (31) 228 (505)	< 0.0001 < 0.0001
	Estimated glomerular filtration rate (eGFR), mL/min/1.73 m 2 eGFR category: G1 (> 90), n (%) UAE category: Normoalbuminuria, n (%) 166 (58.0)		81 (30) 166 (54.8) 99 (47.1)	84 (28) 108 (41.4) 33 (30.0)	83 (31) 12 (16.4) 5 (16.1)	0.56 < 0.0001 < 0.0001
	eGFR category: G1 (> 90), n (%) G2 (90-60), n (%) Microalbuminuria, n (%)	108 37.8)		44 (41.5) 99 (32.7) 93 (44.3)	131 (45.5) 93 (35.6) 50 (45.4)	111 (45.7) 18 (24.7) 10 (32.3)	0.31
		G2 (90-60), n (%) G3 (59-30), n (%) Macroalbuminuria, n (%)	12 (4.2)		33 (31.1) 33 (10.9)	18 (8.6)	105 (36.4) 50 (19.2) 27 (24.6)	72 (29.6) 27 (37.0) 16 (51.6)
	G3 (59-30), n (%) G4 (< 30-16), n (%) eGFR, mL/min/1.73 m 2			109 (16)		24 (22.7) 5 (1.6)		76 (9)	40 (3.9) 10 (3.8)	47 (8)	46 (18.9) 16 (21.9) 23 (4)	< 0.0001
	G4 (< 30-16), n (%) Urinary glucose, mmol/L* Urinary glucose, mmol/L*			5.2 (89.7)		5 (4.7) 5.5 (86.1) 2.3 (37.1)	12 (4.2) 1.8 (32.8) 1.5 (0.6-17.8)	14 (5.8) 2.2 (17.1) 1.8 (7.0)	0.04 < 0.0001
	Urinary glucose, mmol/L* FRGLU, %* FRGLU, %*			99.68 (5.38)	0.9 (1.2) 99.64 (6.53) 99.83 (1.99)	1.5 (3.8) 99.86 (1.97) 99.80 (2.32)	69.9 (197.8) 99.47 (1.74) 99.47 (2.10)	< 0.0001 0.002 0.26
	FRGLU, %* Quantitative data expressed as means (SD) or *medians (IQR); analysis of covariance (ANCOVA) by *Kruskal-Wallis or Pearson's chi-squared 99.90 (0.15) 99.90 (0.41) 96.36 (12.57) < 0.0001
	Quantitative data expressed as means (SD) or *medians (IQR); analysis of covariance (ANCOVA) by *Kruskal-Wallis or Pearson's chi-squared test (qualitative data); P ≤ 0.05 considered significant;
	test (qualitative data); P ≤ 0.05 considered significant; FRGLU: fractional reabsorption of glucose
	FRGLU: fractional reabsorption of glucose

Acknowledgments

The authors are grateful to their patients for their participation, and to the healthcare staff of the Diabetes Department, Bichat-Claude Bernard University Hospital in Paris, France, for their expert assistance in this investigation.

LP reports grants, personal fees and non-financial support from Novo Nordisk and Sanofi, personal fees and non-financial support from Eli Lilly, and non-financial support from Servier.

KM reports personal fees and non-financial support from Novo Nordisk and Sanofi, and nonfinancial support from VitalAir.

SH reports personal fees and non-financial support from AstraZeneca, Bristol-Myers Squibb, Janssen, MSD and Sanofi, and personal fees from Abbott, Boehringer Ingelheim, Eli Lily, Novartis, Novo Nordisk, Servier and Takeda.

RR is an advisory panel member for AstraZeneca, AbbVie, Sanofi, MSD, Eli Lilly, Janssen, Novo Nordisk and Physiogenex, and has been a speaker for Bayer and Servier, and received research funding and provided research support to Danone Research, Amgen, Sanofi and Novo Nordisk.

MM reports personal fees from Abbott, Intarcia, Eli Lilly, MSD, Novo Nordisk, Sanofi and Servier, grants from Novo Nordisk, Sanofi, Servier, MSD and Novartis, and non-financial support from Novo Nordisk. OM, YA, MHF, FF and GV all declare no competing interest.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Data availability

The dataset analyzed during the present study is not publicly available due to hospital records policy.

Contribution statement

OM and MM designed the study, researched data and drafted the manuscript. GV researched data and drafted the manuscript. YA and MHF researched data, contributed to the discussion and reviewed/edited the manuscript. LP, FF, SH and RR contributed to the discussion and reviewed/edited the manuscript. MM is the guarantor of this work and, as such, had full access to all study data, and takes full responsibility for the integrity of the data and accuracy of the data analysis. All authors approved the submitted version of the manuscript.