Stéphanie Ragot
email: stephanie.ragot@univ-poitiers.fr.

Pierre-Jean Saulnier

Gilberto Velho

Elise Gand

Astrid De Hauteclocque

Yousri Slaoui

Louis Potier

Philippe Sosner

Jean-Michel Halimi

Philippe Zaoui

Vincent Rigalleau

Frederic Fumeron

Ronan Roussel

Michel Marre

Samy Hadjadj

Dynamic Changes in Renal Function Are Associated With Major Cardiovascular Events in Patients With Type 2 Diabetes

The pattern of renal function decline prior to cardiovascular (CV) events in type 2 diabetes is not well known. Our aim was to describe the association between renal function trajectories and the occurrence of a CV event.

RESEARCH DESIGN AND METHODS

We considered patients with type 2 diabetes from the Survie, Diabete de type 2 et Genetique (SURDIAGENE) study (discovery cohort) and the Non-Insulin-Dependent Diabetes, Hypertension, Microalbuminuria or Proteinuria, Cardiovascular Events, and Ramipril (DIABHYCAR) study (replication cohort). Global patterns of estimated glomerular filtration rate (eGFR) (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]) and serum creatinine (SCr) prior to a major CV event (MACE) or last update were determined using a linear mixed-effects model and annual individual slopes computed by simple linear regression.

RESULTS

In the 1,040 participants of the discovery cohort, establishment of global patterns including 22,227 SCr over 6.3 years of follow-up showed an annual eGFR decline and an annual SCr increase that were significantly greater in patients with MACE compared with patients without (23.0 and 21.7 mL/min/1.73 m 2 /year and +10.7 and +4.0 mmol/L/year, respectively; P < 0.0001 for both). Median annual individual slopes were also significantly steeper in patients with MACE, and adjusted risk of MACE was 4.11 times higher (3.09-5.45) in patients with rapid decline in eGFR (change less than 25 mL/min/1.73 m 2 /year). Consideration of renal function trajectories provided significant additive information helping to explain the occurrence of MACE for both SCr and eGFR (P IDI < 0.0001 and P = 0.0005, respectively). These results were confirmed in the replication cohort.

CONCLUSIONS

Renal function decline was associated with a higher risk of MACE. The pattern of renal function decline, beyond baseline kidney function, is an independent factor of CV risk.

Diabetes is considered a global nontransmissible epidemic, of which the prevalence has been increasing worldwide. Interestingly, cardiovascular (CV) disease is the first cause of death in people with diabetes (1). In addition to traditional risk factors, such as lipids or smoking, kidney disease is an important contributor to CV disease (2).

Indeed, numerous studies have established that kidney disease was associated with CV hard end points [START_REF] Anavekar | Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction[END_REF], both in the general population and in secondary prevention of CV disease.

Due to its association with kidney and CV diseases on the one hand and to recommended monitoring of serum creatinine (SCr) on the other hand, diabetes is an excellent model for study of the temporal relationship between SCr trajectories and CV outcomes. One key question in this context is whether the dynamic process of renal function decline is a contributor to CV disease beyond baseline kidney function.

The relationship between estimated glomerular filtration rate (eGFR) slope and major CV outcomes is a recent finding, established in studies on large populations. It has consequently been suggested that eGFR slope can be of prognostic interest. However, some of these studies were retrospective [START_REF] Perkins | Variability in estimated glomerular filtration rate is an independent risk factor for death among patients with stage 3 chronic kidney disease[END_REF], and no study has tried to determine whether this was true in patients with type 2 diabetes [START_REF] Turin | Change in the estimated glomerular filtration rate over time and risk of all-cause mortality[END_REF][START_REF] Coresh | Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality[END_REF]. That is why we aimed to study the association between renal function patterns and the occurrence of CV events in a cohort study of patients with type 2 diabetes, using a complementary replication cohort to validate our findings.

RESEARCH DESIGN AND METHODS

Discovery Cohort Patients

Data are from the Survie, Diabete de type 2 et Genetique (SURDIAGENE) study, a prospective monocentric cohort of 1,468 patients with type 2 diabetes recruited and followed at the University Hospital of Poitiers (France) between 2002 and 2012. A description of the cohort has been presented elsewhere [START_REF] Hadjadj | DIABHYCAR Study Group; DIAB2NEPHROGENE Study Group; SURDIAGENE Study Group. Prognostic value of the insertion/deletion polymorphism of the ACE gene in type 2 diabetic subjects: results from the Non-insulin-dependent Diabetes, Hypertension, Microalbuminuria or Proteinuria, Cardiovascular Events, and Ramipril (DIABHYCAR), Diabete de type 2, Nephropathie et Genetique (DIAB2NEPHROGENE), and Survie, Diabete de type 2 et Genetique (SURDIAGENE) studies[END_REF]. The study protocol received ethics approval (Comit é de Protection des Personnes Ouest III), and written informed consent was given by each participant.

To be considered in the present analysis, patients had to fulfill the following criteria: free of renal replacement therapy at entry in the cohort, normal to moderately reduced kidney function (eGFR estimated by the Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] formula; $30 mL/min/1.73 m 2 ), and at least three SCr determinations during follow-up.

Variables and Their Measurement in the Discovery Cohort

Baseline data used for the analysis included demographic information, diabetes duration, smoking status, systolic and diastolic blood pressure, HbA 1c , SCr, and comorbid conditions. The SCr determinations included in this study were all available determinations recorded in the Poitiers hospital biological database. We deleted values determined after an occurrence of end-stage renal disease requiring renal replacement therapy or after the first study outcome. A distinction was made between determinations performed during an overnight hospital stay (in-hospital determinations) and determinations performed during consultations (outpatient determinations). SCr was measured by nephelometry using a MODULAR System P (Roche Diagnostics GmbH, Mannheim, Germany).

The eGFR was calculated using the CKD-EPI equation [START_REF] Levey | A new equation to estimate glomerular filtration rate[END_REF]. HbA 1c was determined using a high-performance liquid chromatography method performed with an ADAMS A1c HA-8160 analyzer (normal values 4.0-6.0%; Menarini, Florence, Italy).

The study outcome was the occurrence of a major CV event (MACE), a composite criterion combining CV mortality, nonfatal myocardial infarction, and nonfatal stroke, as is widely used [START_REF] The | Heart Outcomes Prevention Evaluation) Study: the design of a large, simple randomized trial of an angiotensin-converting enzyme inhibitor (ramipril) and vitamin E in patients at high risk of cardiovascular events. The HOPE study investigators[END_REF]. Living status and CV end points were determined from patients' hospital records and interviews of their general practitioners, every 2nd year since 2007. The present analysis takes into account data of the last updating performed at the end of 2013. All events were adjudicated by an independent adjudication committee (see composition in ACKNOWLEDGMENTS). Cardiovascular death was defined as death due to causes listed in the World Health Organization ICD-10, chapter IX (diseases of the circulatory system) (2007).

Replication Cohort Patients

Non-Insulin-Dependent Diabetes, Hypertension, Microalbuminuria or Proteinuria, Cardiovascular Events, and Ramipril (DIABHYCAR) is a clinical trial conducted in people with type 2 diabetes selected on the basis of persistent microalbuminuria (urinary albumin concentration [UAC] in the range 20-200 mg/L) or macroalbuminuria (UAC .200 mg/L) without renal failure (plasma creatinine ,150 mmol/L) at baseline [START_REF] Li Èvre | DIABHYCAR Study Group. The non-insulindependent diabetes, hypertension, microalbuminuria or proteinuria, cardiovascular events, and ramipril (DIABHYCAR) study: design, organization, and patient recruitment[END_REF]. The trial tested the effect of a low dose of ramipril, an ACE inhibitor (ACEI), on the incidence of CV and/or renal events. The median duration of follow-up was 4.7 years. Results were negative regarding ramipril and were published previously [START_REF] Marre | DIABHYCAR Study Investigators. Effects of low dose ramipril on cardiovascular and renal outcomes in patients with type 2 diabetes and raised excretion of urinary albumin: randomised, double blind, placebo controlled trial (the DIABHYCAR study)[END_REF]. Participants gave written informed consent, and the ethics committee of Angers University Hospital approved the study protocols.

We included in our analysis French patients with type 2 diabetes with three or more SCr determinations during followup. SCr determinations were performed locally. HbA 1c was determined using a high-performance liquid chromatography method performed using a DIAMAT analyzer (normal values 4.0-5.6%; Bio-Rad, Richmond, CA)

Statistical Analysis

Analyses were conducted in SAS for windows, version 9.3 (SAS Institute, Cary, NC). Statistical significance was set at a P value ,0.05.

Characteristics of patients are presented as frequency and percentage, means and SDs, or medians (25th-75th percentiles). Patients with MACE and without MACE were compared with the x 2 test for categorical variables and the student t test or the Mann-Whitney U test for continuous variables.

To describe renal function patterns, all determinations recorded during the window period (in-hospital plus outpatient determinations) were considered for the main analysis, and determinations exclusively recorded in the outpatient setting were taken into account in a second analysis.

Trajectories for creatinine and for eGFR were studied in patients with MACE during follow-up and in patients without MACE. Both global patterns and individual patterns for these renal markers were established. A population approach determined global patterns using the linear mixed-effects model, where random intercepts and random coefficients (slopes) were calculated and tested using the likelihood ratio test. Trajectories were calculated using the fixed-portion linear predictor plus the value corresponding to the predictor random effects. Differences in slopes between groups were evaluated in the linear mixed-effects model. The observation period started at the date of the first event of the composite outcome for the patients having experienced an event and at the last screening or consultation date for patients without event (right-justified data). Patients were then traced backward to the date of entry in the cohort, so that creatinine and eGFR trajectories reflected the series of determinations recorded before the event.

Individual patterns (slope) were established calculating the absolute annual change in creatinine and eGFR for each patient using the simple linear regression coefficient. eGFR slope was recoded in a binary variable according to a threshold proposed in the literature (25 mL/min/1.73 m 2 /year) [START_REF] Levin | Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward[END_REF]. The cutoff value for creatinine slope was estimated from simple linear regression between eGFR slope and creatinine slope, as there was no advocated threshold value in the literature for creatinine slope. Survival curves were built by the Kaplan-Meier method and compared using a log-rank test.

Univariate and multivariate stepwise Cox proportional hazard regression analyses were performed to identify factors associated with MACE. Regarding multivariate analysis, we used a backward manual procedure performed on a maximal model including all factors that were associated with MACE with P , 0.20 in univariate Cox analysis. Results are given as hazard ratios with 95% CIs.

The improvement in Cox model performance given by adding creatinine or eGFR slope was calculated using the integrated discrimination improvement (IDI) index. Results are given as P values of the IDI, which indicate significance of the improvement.

RESULTS

Characteristics of the Study Populations

Discovery Cohort

Of the 1,468 patients enrolled in the SURDIAGENE cohort, 1,140 patients were considered in the present analysis of the discovery cohort and yielded 22,227 creatinine determinations over a period of 6.3 (3.6-8.9) years (Fig. 1A). creatinine determinations (Fig. 1B). MACE events occurred in 180 patients (14.76 Baseline characteristics according to the occurrence of MACE are described in Table 1 for both cohorts. Of note, the studied treatment allocated by randomization was not associated with MACE.

Global Patterns of Renal Markers

Discovery Cohort

Global patterns of SCr and eGFR established using linear mixed-effect models showed an SCr annual increase greater in patients with a MACE event compared with patients without (10.7 and 4.0 mmol/L/year, respectively; P , 0.0001). Similarly, eGFR annual decline was greater in patients with MACE than in those without (23.0 in patients with MACE and 21.7 mL/min/1.73 m 2 /year in those without; P , 0.0001).

When considering outpatient determinations only, in a sensitivity analysis made on 9,026 determinations, SCr annual increase and eGFR annual decline were also greater in patients with MACE compared with patients without (SCr annual increase: 7.4 vs. 3.1 mmol/L/year; eGFR annual decline: 22.5 vs. 21.5 mL/min/1.73 m 2 /year, respectively, in patients with and without event; P = 0.0003 and 0.0002, respectively).

Replication Cohort

In the DIABHYCAR cohort, mixed-effect models showed, as in the discovery cohort, a greater SCr annual increase in patients with MACE event compared with patients free of event (5.1 and 1.8 mmol/L/year, respectively; P , 0.0001). Regarding eGFR trajectory, a greater eGFR annual decline was found in patients with MACE (22.6 mL/min/1.73 m 2 /year) than in patients without an event (21.1 mL/min/1.73 m 2 /year; P , 0.0001). Patients with an annual increase of SCr .14.0 mmol/L/year were significantly more likely to develop MACE than patients with a lower change (P log-rank ,0.0001) (Fig. 2A). In the same manner, an absolute annual change in eGFR less than 25 mL/min/1.73 m 2 /year was associated with a higher risk of MACE (P log-rank ,0.0001) (Fig. 2B). Of note, these associations between renal function pattern and risk of MACE were consistent in all quartiles of baseline creatinine (P , 0.001 for all) (Supplementary Fig. 2). Moreover, when stratifying on the personal history of CV disease, the association remained significant (P , 0.001 in both subgroups).

Individual Patterns of Renal Markers

After adjustment on baseline renal function, CV disease history, and other prognostic factors, the risk of MACE was 3.15 times higher (2.25-4.41) in patients with an increase in SCr .14.0 mmol/L/year and 4.11 times higher (3.09-5.45) in patients with rapid renal function decline (change in eGFR less than 25 mL/min/1.73 m 2 /year) (Table 2). Using renal pattern as a dependent variable in Cox models significantly improved the performance of the models (P , 0.0001 with SCr slope; P = 0.0005 with eGFR slope). These results were unchanged when only out determinations were taken into account to build individual renal patterns.

Replication Cohort

The analysis made on the DIABHYCAR cohort confirmed that patients with an SCr change .14.0 mmol/L/year or an eGFR change less than 25 mL/min/1.73 m 2 /year were at higher risk of MACE even after adjustment on the other contributory factors (Table 2). Cox model performance was significantly improved by adding renal function decline as a dependent variable (P , 0.0001 with SCr slope; P , 0.002 with eGFR slope).

CONCLUSIONS

In our discovery cohort, we found that creatinine and eGFR trajectories were significantly associated with the occurrence of a MACE in type 2 diabetes; a more rapid renal function decline was associated with a higher risk of occurrence of a MACE. This finding was replicated using another cohort from the same geographical origin. It was also supported by different sensitivity analyses taking into consideration both the whole discovery cohort and outpatient SCr determinations.

Whereas most of the studies evaluating dynamic changes of renal function report eGFR trajectories, the analyses we performed assessed both the pattern of SCr and estimated GFR. We believe that SCr is a good clinical biomarker, as its validity is not influenced by the validity of the CKD-EPI formula. In addition, consideration of SCr instead of estimated GFR rules out the effect of age change during followup. Last, whereas the CKD-EPI formula proved to be well correlated with renal function, its value for repeated measures proved to be lesser, rendering SCr an interesting biomarker to evaluate follow-up changes [START_REF] Perkins | Detection of renal function decline in patients with diabetes and normal or elevated GFR by serial measurements of serum cystatin C concentration: results of a 4-year follow-up study[END_REF][START_REF] Rossing | Monitoring kidney function in type 2 diabetic patients with incipient and overt diabetic nephropathy[END_REF][START_REF] Gaspari | GFR Study Investigators. The GFR and GFR decline cannot be accurately estimated in type 2 diabetics[END_REF]. Consequently, not only eGFR trajectory but also SCr trajectory should be considered as a means of capturing the dynamics of renal function modifications with regard to CV diseases.

Our results proved to be consistent in the two studied cohorts: SURDIAGENE as a discovery cohort and DIABHYCAR as a replication cohort. In both studies, adjudication of outcomes rendered our findings more reliable. Even though both cohorts are composed of French patients with type 2 diabetes, they are actually rather dissimilar; whereas SURDIAGENE is a single-center hospitalbased cohort, patients from DIABHYCAR were recruited on the occasion of a clinical trial managed by their general practitioners. The determinations of SCr during follow-up were correspondingly different; in the SURDIAGENE cohort, as in real-life situations, frequency of SCr determinations and their timing and follow-up time can largely vary, whereas in clinical trials such as DIABHYCAR, the determinations are preplanned and controlled. In addition, the SURDIAGENE and DIABHYCAR populations were different with regard to diabetes treatment and renal function; whereas SURDIAGENE participants were recruited regardless of renal function and diabetes treatment, participants in the DIABHYCAR study were recruited with SCr ,150 mmol/L and were treated with oral antidiabetic drugs only without insulin. The consistency of the results in both cohorts strongly supports the generalization of our results. Last, our sensitivity analysis proved that when focusing on SCr determinations from outpatients only, the data were unchanged.

Analysis of decline in renal function for diagnosis of clinical outcomes has previously been used. In type 1 diabetes, Skupien et al. [START_REF] Skupien | The early decline in renal function in patients with type 1 diabetes and proteinuria predicts the risk of end-stage renal disease[END_REF] established the prognostic role of eGFR changes for the prognosis of end-stage renal disease, and it has been confirmed in the general population [START_REF] Coresh | Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality[END_REF]. In coronary artery disease patients, the eGFR pattern likewise proved to be prognostic for vascular events [START_REF] Rein | Serial decline of kidney function as a novel biomarker for the progression of atherothrombotic disease[END_REF]. This was also suggested in a Japanese population, in spite of the fact that the precise pattern of eGFR previous to CV outcome could not be ascertained, leaving some doubt as to whether eGFR decline was the cause or the consequence of CV events [START_REF] Nagai | Annual decline in estimated glomerular filtration rate is a risk factor for cardiovascular events independent of proteinuria[END_REF]. Interestingly, the just-mentioned studies were not performed in specific populations with diabetes. The current study has established the value of decline of kidney function beyond baseline SCr value, in accordance with the Atherosclerosis Risk in Communities (ARIC) study focusing on patients with CKD stage 3 [START_REF] Matsushita | Change in estimated GFR associates with coronary heart disease and mortality[END_REF].

We used a minimal number of three SCr determinations at variance with the data from the CKD consortium involving two SCr determinations in 1-3 years (6). However, our findings proved to be very consistent with their results. The magnitude of the effect in our two cohorts was much greater than in a general Canadian population [START_REF] Coresh | Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality[END_REF], a finding that may be related to the high CV risk of patients with type 2 diabetes.

It is possible to speculate about the relevance of the decline of renal function beyond baseline value. We can imagine that the clearance of a deleterious factor is affected by the dynamic Some epidemiological associations can be reminded to explain the CV impact of the decline of renal function beyond baseline value. Renal function has been shown to be associated with many different changes, such as lipids [START_REF] Gröne | The role of lipids in nephrosclerosis and glomerulosclerosis[END_REF][START_REF] Bonnet | Potential influence of lipids in diabetic nephropathy: insights from experimental data and clinical studies[END_REF][START_REF] Muntner | Plasma lipids and risk of developing renal dysfunction: the Atherosclerosis Risk in Communities study[END_REF], blood pressure [START_REF] Barzilay | Predisposition to hypertension: risk factor for nephropathy and hypertension in IDDM[END_REF][START_REF] De Galan | Lowering blood pressure reduces renal events in type 2 diabetes[END_REF][START_REF] Jerums | Relationship of progressively increasing albuminuria to apoprotein(a) and blood pressure in type 2 (non-insulin-dependent) and type 1 (insulindependent) diabetic patients[END_REF][START_REF] Nelson | Parental hypertension and proteinuria in Pima Indians with NIDDM[END_REF], insulin resistance [START_REF] Thorn | FinnDiane Study Group. Metabolic syndrome in type 1 diabetes: association with diabetic nephropathy and glycemic control (the FinnDiane study)[END_REF][START_REF] Yip | Insulin resistance in insulindependent diabetic patients with microalbuminuria[END_REF], or low-grade inflammation [START_REF] Clausen | Plasma concentrations of VCAM-1 and ICAM-1 are elevated in patients with type 1 diabetes mellitus with microalbuminuria and overt nephropathy[END_REF][START_REF] Lim | Inflammation in diabetic nephropathy[END_REF][START_REF] Navarro-Gonz Ález | Inflammatory molecules and pathways in the pathogenesis of diabetic nephropathy[END_REF]. Unfortunately, it was not feasible to take such changes into account as longterm serial determinations of these variables were unavailable in most of the patients considered in our analysis.

Some limitations in our study must be noted. Renal function decline can be impacted by many drugs such as ACEIs [START_REF] Apperloo | A short-term antihypertensive treatment-induced fall in glomerular filtration rate predicts longterm stability of renal function[END_REF]. Our cohort was not designed to take drug modifications during followup into account. However our primary aim was to evaluate the association between renal function decline and occurrence of MACE rather than the determinants of renal function decline, such as drugs or comorbidities

In the SURDIAGENE cohort, all determinations were performed in the same laboratory but with no prespecified time frame. However, use of the mixed linear model helped to take this into account as this model does not require a specific time lapse between determinations. The dynamic process we were dealing with was not always linear, which might blur interpretation of the dynamic changes, particularly when considering eGFR or Scr individual slopes. Exclusion of nonlinear trajectories (n = 319 [i.e., 28%] and n = 255 [i.e., 22%] regarding trajectories of SCr and eGFR built with all-determinations in SURDIAGENE cohort) did not induce any modification of our conclusion (data not shown).

In the DIABHYCAR study, longitudinal SCr determinations were determined locally, possibly entailing some heterogeneity, but the results were satisfactorily robust. It is worth recalling that as we used SCr, our findings should be interpreted cautiously. It would be extremely interesting to consider another marker, such as cystatin C, which has proved to be of higher value compared with SCr, for the prognosis of severe outcomes [START_REF] Shlipak | CKD Prognosis Consortium. Cystatin C versus creatinine in determining risk based on kidney function[END_REF].

Analysis with the IDI index showed that the trajectory of the renal function adds significant information to baseline creatinine. This result suggests that the dynamic process of renal function could be used as a prognostic marker of MACE, as it could easily be integrated in clinical practice.

In conclusion, the consideration of a simple and inexpensive biomarker, analyzed using a dynamic pattern, proved to be of prognostic value for CV outcomes in two complementary cohorts of patients with type 2 diabetes. Our data strongly support the systematic use of serial measurements of SCr and/or eGFR for fine tuning the prognosis of patients with type 2 diabetes. They should be carried out with adequate computing tools or web application (see eGFR calculator of renal function decline at http://www.sfdiabete.org/ renalfunctiondeclinecalculator). conflicts of interest relevant to this article were reported.

The analysis and interpretation of the data was performed without the participation of any of these organizations and companies.

  The composite outcome occurred in 218 patients (30.64 per 1,000 personyears [95% CI 26.57-34.71]), with 41 patients presenting a nonfatal stroke, 62 patients a nonfatal myocardial infarction, and 115 a CV death. A total of 154 CV deaths (21.13 per 1,000 person-years [95% CI 17.79-24.47]) and 282 all-cause deaths (38.69 per 1,000 person-years [95% CI 34.17-43.20]) were observed. Replication Cohort Regarding the replication cohort, 2,572 patients of the 3,137 French patients included in the DIABHYCAR trial were considered for analysis, yielding 11,387

Figure 1 -

 1 Figure 1-Analyzed populations from SURDIAGENE cohort (A) and DIABHYCAR trial (B).

  Discovery CohortDichotomization of annual change in SCr was made according to the result of simple linear regression between eGFR change and SCr change, translating an annual change of 25 mL/min/1.73

m 2 /

 2 year for eGFR to 14.0 mmol/L/year for SCr.Determination of individual patterns showed median individual SCr slopes significantly steeper in patients with a MACE compared with patients free of MACE (5.5 mmol/L/year [0.9-18.9] vs. 1.0 mmol/L/year [21.0 to 24.7], respectively; P , 0.0001) (Supplementary Table

1

 1

  ) and median eGFR slopes significantly greater in patients with a MACE (23.3 mL/min/1.73 m 2 /year [27.5 to 20.6] vs. 21.4 mL/min/1.73 m 2 /year [23.7 to 0.2] in patients without MACE; P , 0.0001). Distribution of these annual rates of change in SCr and eGFR are shown in Supplementary Fig. 1.

Figure 2 -

 2 Figure 2-Kaplan-Meier plot of the probability of CV survival according to yearly SCr change (A) and yearly eGFR change (B). Thick lines represent patients with rapid renal function decline (.14 mmol/L/year for SCr increase and less than 25 mL/min/1.73 m 2 /year for eGFR change) and thin lines represent the other patients.

  per 1,000 person-years [95% CI 12.60-16.92]), with 46 patients presenting a nonfatal stroke, 48 patients a nonfatal myocardial infarction, and 86 a CV death.

	A total of 97 CV deaths (7.87 per 1,000
	person-years [95% CI 6.30-9.43]) and
	235 all-cause deaths (19.06 per 1,000
	person-years [95% CI 16.62-21.50]) oc-
	curred during follow-up.

Table 1 -

 1 Baseline characteristics according to the occurrence of MACE in discovery and replication cohorts

			Discovery cohort (SURDIAGENE)			Replication cohort (DIABHYCAR)
		All	Event	No event		All	Event	No event
		n = 1,140	n = 218	n = 922	P value	n = 2,572	n = 180	n = 2,392	P value
	Sex: men/women,	657/483	145/73	512/410	0.003	1,888/684	140/40	1,748/644	0.17
	n (%)	(58%/42%)	(67%/33%)	(56%/44%)		(73%/27%)	(78%/22%)	(73%/27%)
	Age (years)	65 6 11	69 6 10	64 6 11	,0.0001	65 6 8	6 86 8	6 56 8	,0.0001
	Known diabetes
	duration (years)	14 6 10	17 6 10	13 6 10	,0.0001	10 6 8	1 16 8	1 06 8	0.04
	BMI (kg/m 2 )	3 1 6 6	3 16 6	3 26 6	0 . 0 9	2 96 5	2 96 4	2 96 5	0.22
	Active smoking:
	n (%)	122 (11%)	21 (10%)	101 (11%)	0.56	369 (17%)	22 (14%)	347 (17%)	0.31
	Systolic blood
	pressure (mmHg)	131 6 17	135 6 19	131 6 17	0.004	145 6 14	147 6 12	147 6 14	0.06
	Diastolic blood
	pressure (mmHg)	72 6 11	72 6 11	72 6 11	0.78	82 6 8	8 36 8	8 26 8	0.36
	History of CV
	disease*: n (%)	210 (18%)	71 (33%)	139 (15%)	,0.0001	208 (8%)	31 (17%)	177 (7%)	,0.0001
	Myocardial
	infarction	163 (14%)	57 (26%)	106 (11%)	,0.0001	130 (5%)	18 (10%)	112 (5%)	0.002
	Stroke	62 (5%)	21 (10%)	41 (4%)	0.002	88 (3%)	18 (10%)	70 (3%)	,0.0001
	HbA 1c (%)	7.83 6 1.54	8.06 6 1.45	7.77 6 1.56	0.02	7.83 6 1.73	8.16 6 1.88	7.81 6 1.71	0.008
	HbA 1c (mmol/mol)	62.05 6 16.83 64.55 6 15.89 61.46 6 17.00	0.02	62.13 6 18.86 65.72 6 20.57 61.85 6 18.70	0.008
	UAC (mg/L)	22 (8-79)	51 (12-256)	19 (8-66)	,0.0001 72 (39-172)	90 (42-298)	71 (39-166)	0.003
	SCr (mmol/L)	86.20 6 26.04 96.08 6 30.28 83.87 6 24.37 ,0.0001 88.73 6 19.26 93.44 6 19.87 88.38 6 19.17 0.0007
	eGFR (mL/min/
	1.73 m 2 )	7 6 . 4 8 6 20.67 68.17 6 21.89 78.45 6 19.88 ,0.0001 74.98 6 16.98 70.11 6 15.93 75.35 6 17.00 ,0.0001
	Drugs
	Insulin	681 (60%)	161 (74%)	520 (57%)	,0.0001	0	0	0
	ACEIs or ARBs	710 (60%)	159 (73%)	551 (60%)	0.0003	151 (6%)	14 (8%)	137 (6%)	0.26
	Diuretics	506 (44%)	123 (56%)	383 (42%)	,0.0001	554 (21%)	42 (23%)	512 (21%)	0.54
	Calcium
	antagonists	342 (30%)	82 (38%)	260 (28%)	0.007	762 (30%)	73 (41%)	689 (29%)	0.0009
	b-Blockers	387 (34%)	85 (39%)	302 (33%)	0.09	480 (19%)	40 (22%)	440 (18%)	0.21
	Statins	523 (46%)	105 (48%)	418 (45%)	0.47	932 (36%) ¶	64 (36%) ¶	868 (36%) ¶	0.84
	Fibrates	131 (11%)	18 (8%)	113 (12%)	0.09

Values for continuous variables are given as mean 6 SD or median [25th-75th percentile]. Event was defined as MACE (CV death, nonfatal myocardial infarction, or nonfatal stroke). ARB, angiotensin receptor blocker. *History of myocardial infarction and/or history of stroke. ¶In DIABHYCAR study, the distinction between fibrates and statins was not available.

Table 2 -

 2 Risk of MACE by annual percentage change in eGFR or in SCr adjusted for baseline covariates SCr slope (.14 mmol/L vs. #14 mmol/L). b Best fit model obtained from a maximal model containing the following: sex, age, known diabetes duration (years), systolic blood pressure (mmHg), history of CV disease, calcium antagonists, HbA 1c (%), log UAC (mg/L), SCr (mmol/L) measured at baseline, and SCr slope (.14 mmol/L vs. #14 mmol/L). c Best fit model obtained from a maximal model containing the following: known diabetes duration (years), BMI (kg/m 2 ), systolic blood pressure (mmHg), history of CV disease, insulin, ACEIs or ARBs, diuretics, calcium antagonists, b-blockers, fibrates, HbA 1c (%), log UAC (mg/L), eGFR (mL/min/1.73 m 2 ) measured at baseline and eGFR slope (less than 25 mL/min/1.73 m 2 vs. $5 mL/min/1.73 m 2 ). d Best fit model obtained from a maximal model containing the following: known diabetes duration (years), systolic blood pressure (mmHg), history of CV disease, calcium antagonists, HbA 1c (%), log UAC (mg/L), eGFR (mL/min/1.73 m 2 ) measured at baseline, and eGFR slope (less than 25 mL/min/1.73 m 2 vs. $5 mL/min/1.73 m 2 ). In models 3 and 4, age and sex were not included in the maximal model because they were already taken into account in the eGFR calculation. e Log-transformed data.

	Variable	Hazard ratio	95% CIs	P value	P IDI
	Renal function pattern: SCr slope
	SURDIAGENE cohort
	Model 1 a
	History of CV disease	1.85	(1.39-2.44)	,0.0001
	Age at baseline (years)	1.05	(1.04-1.07)	,0.0001
	Female sex	0.73	(0.54-0.98)	0.04
	Diuretics at baseline	1.45	(1.10-1.92)	0.008
	Baseline HbA 1c (%)	1.10	(1.01-1.20)	0.04
	Urinary baseline albumin concentration e (mg/L)	1.24	(1.04-1.49)	0.02
	SCr slope .14 mmol/L	3.15	(2.25-4.41)	,0.0001	,0.0001
	DIABHYCAR cohort
	Model 2 b
	History of CV disease	2.23	(1.50-3.30)	,0.0001
	Age at baseline (years)	1.06	(1.04-1.08)	,0.0001
	Baseline HbA 1c (%)	1.14	(1.04-1.24)	0.003
	Urinary baseline albumin concentration e (mg/L)	1.42	(1.05-1.91)	0.02
	SCr slope .14 mmol/L	1.66	(1.00-2.76)	0.049	,0.0001
	Renal function pattern: eGFR slope
	SURDIAGENE cohort
	Model 3 c
	History of CV disease	2.20	(1.67-2.90)	,0.0001
	Known diabetes duration (years)	1.02	(1.00-1.03)	0.009
	Diuretics at baseline	1.47	(1.12-1.94)	0.006
	Baseline eGFR (mL/min/1.73 m 2 )	0.98	(0.98-0.99)	,0.0001
	eGFR slope less than 25 mL/min/1.73 m 2	4.11	(3.09-5.45)	,0.0001	0.0005
	DIABHYCAR cohort
	Model 4 d
	History of CV disease	2.43	(1.64-3.59)	,0.0001
	Baseline HbA 1c (%)	1.11	(1.02-1.20)	0.02
	Baseline eGFR (mL/min/1.73 m 2 )	0.98	(0.97-0.99)	,0.0001
	eGFR slope less than 25 mL/min/1.73 m 2	2.24	(1.59-3.15)	,0.0001	0.002
	IDI evaluated the additive information of eGFR slope (or SCr slope) for risk of MACE. a Best fit model obtained from a maximal model containing the
	following: sex, age, known diabetes duration (years), BMI (kg/m 2 ), systolic blood pressure (mmHg), history of CV disease, insulin, ACEIs or
	angiotensin receptor blockers (ARBs), diuretics, calcium antagonists, b-blockers, fibrates, HbA 1c (%), log UAC (mg/L), SCr (mmol/L) measured at
	baseline, and

Renal Function Changes and Cardiovascular Risk Diabetes Care

Renal Function Changes and Cardiovascular Risk Diabetes Care

care.diabetesjournals.orgRagot and Associates 5

Renal Function Changes and Cardiovascular Risk Diabetes Care

Renal Function Changes and Cardiovascular Risk Diabetes Care

Acknowledgments. The authors warmly thank all patients included and followed in the cohort study for their kind participation in this research. Their general practitioners are acknowledged for their help in collecting clinical information. The authors acknowledge the following recruiting physicians: Fr éd érique Duengler, Louis Labbé, Aurélie Miot, Xavier Piguel, Florence Torremocha, Pierre-Jean Saulnier, and Richard Maréchaud (CHU de Poitiers). The following individuals provided secretarial and technical assistance: Vanessa Le Berre (CHU de Poitiers; Pôle DUNE; editorial assistance), all the staff from the Department of Endocrinology and Diabetology, CHU de Poitiers (recruitment), and Sonia Brishoual and the staff of the INSERM CIC 1402, CHU de poitiers (biobanking and data management). G érard Mauco (Department of Biochemistry, CHU de Poitiers) and Thierry Hauet (INSERM U1082, CHU de Poitiers) are acknowledged for helping in biological determinations. Marie Claire Pasquier and Alexandre Pavy (DSIO, CHU de Poitiers) actively contributed to the collection of data in the SURDIAGENE cohort. Jeffrey Arsham (CHU de Poitiers) edited the English of the manuscript. All patient records were reviewed to ascertain the following points: type 2 diabetes, diabetic kidney disease, diabetic retinopathy, and CV disease. The authors warmly thank the clinicians

Author Contributions. S.R. researched data, performed statistical analysis, contributed to the discussion, and wrote the manuscript. P.-J.S., G.V., E.G., A.d.H., and Y.S. researched data, performed statistical analysis, contributed to the discussion, and wrote the manuscript. L.P., P.S., J.-M.H., P.Z., V.R., F.F., and R.R. contributed to the discussion and edited the manuscript. M.M. designed the study, researched data, contributed to the discussion, and edited the manuscript. S.H. designed the study, researched data, contributed to the discussion, and wrote the manuscript. S.H. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.


Laurent Maïmoun1

Anthony Gelis3

Chris Serrand4

• Thibault

Ludovic Humbert5

Vincent Boudousq6

Pascal De Santa-Barbara2

Didier Laux7

Charles Fattal8

Denis Mariano-Goulart1

Laurent Maimoun
email: l-maimoun@chu-montpellier.fr

Alteration of Volumetric Bone Mineral Density Parameters in Men with Spinal Cord Injury

Keywords: Spinal cord injury, 3D-DXA, 3D-SHAPER®, Volumetric bone mineral density, Areal bone mineral density, Trabecular bone, Cortical bone ---

Spinal cord injury (SCI) induces severe losses of trabecular and cortical volumetric bone mineral density (vBMD), which cannot be discriminated with conventional dual-energy X-ray absorptiometry (DXA) analysis. The objectives were to: (i) determine the effects of SCI on areal BMD (aBMD) and vBMD determined by advanced 3D-DXA-based methods at various femoral regions and (ii) model the profiles of 3D-DXA-derived parameters with the time since injury. Eighty adult males with SCI and 25 age-matched able-bodied (AB) controls were enrolled in this study. Trabecular and cortical vBMD, cortical thickness and derived strength parameters were assessed by 3D-SHAPER® software at various femoral subregions. Individuals with SCI had significantly lower integral vBMD, trabecular vBMD, cortical vBMD, cortical thickness and derived bone strength parameters (p < 0.001 for all) in total proximal femur compared with AB controls. These alterations were approximately to the same degree for all three femoral subregions, and the difference between the two groups tended to be greater for cortical vBMD than trabecular vBMD. There were minor differences according to the lesion level (paraplegics vs tetraplegics) for all 3D-DXA-derived parameters. For total proximal femur, the decreasing bone parameters tended to reach a new steady state after 5.1 years for integral vBMD, 7.4 years for trabecular vBMD and 9.2 years for cortical vBMD following SCI. At proximal femur, lower vBMD (integral, cortical and trabecular) and cortical thickness resulted in low estimated bone strength in individuals with SCI. It remains to be demonstrated whether these new parameters are more closely associated with fragility fracture than aBMD.

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Introduction

Spinal cord injury induces bone loss through the acute and dramatic increase in bone resorption activity associated with almost normal bone formation during the first weeks following injury [START_REF] Roberts | Longitudinal study of bone turnover after acute spinal cord injury[END_REF][START_REF] Maimoun | Changes in osteoprotegerin/RANKL system, bone mineral density, and bone biochemicals markers in patients with recent spinal cord injury[END_REF]. Various studies using dual-energy X-ray absorptiometry (DXA), the conventional tool for measuring areal bone mineral density (aBMD, g/cm2), have demonstrated that the value decreases rapidly, with a loss of 30-40% at the femoral neck within the first 2 years after injury, followed by a new steady state [START_REF] Maimoun | Changes in osteoprotegerin/RANKL system, bone mineral density, and bone biochemicals markers in patients with recent spinal cord injury[END_REF][START_REF] Biering-Sorensen | Longitudinal study of bone mineral content in the lumbar spine, the forearm and the lower extremities after spinal cord injury[END_REF]. However, other studies have shown continuous bone loss beyond this period [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Bauman | Continuous loss of bone during chronic immobilization: a monozygotic twin study[END_REF]. The bone deterioration occurs exclusively below the level of injury with a maximum in regions initially subject to weight-bearing, such as hip or tibia [START_REF] Maimoun | Changes in osteoprotegerin/RANKL system, bone mineral density, and bone biochemicals markers in patients with recent spinal cord injury[END_REF].

Bone loss implies a significant weakening of skeletal integrity, which likely influences the 24to 104-fold higher compared with the able-bodied population [START_REF] Frisbie | Fractures after myelopathy: the risk quantified[END_REF], even though their risk of trauma is reduced. The most frequent mechanisms identified for the low-energy lower extremity fractures are forced manoeuvres by the patient or a third party, falls and during transfers [START_REF] Fattal | Osteoporosis in persons with spinal cord injury: the need for a targeted therapeutic education[END_REF]. These fractures most commonly occur at the epiphysis or metaphysis of the distal femur or proximal tibia [START_REF] Fattal | Osteoporosis in persons with spinal cord injury: the need for a targeted therapeutic education[END_REF][START_REF] Morse | Osteoporotic fractures and hospitalization risk in chronic spinal cord injury[END_REF][START_REF] Grassner | Lower extremity fractures in patients with spinal cord injury characteristics, outcome and risk factors for non-unions[END_REF], where the bone loss is most severe [START_REF] Biering-Sorensen | Bone mineral content of the lumbar spine and lower extremities years after spinal cord lesion[END_REF][START_REF] Zheng | Bone mineral density at the distal femur and proximal tibia and related factors during the first year of spinal cord injury[END_REF]. As fragility fractures result in increased healthcare costs, short-term hospitalisation, increased disability and mortality [START_REF] Vestergaard | Fracture rates and risk factors for fractures in patients with spinal cord injury[END_REF][START_REF] Carbone | Morbidity following lower extremity fractures in men with spinal cord injury[END_REF], strategies for identifying those individuals at high risk of these fracture should be further developed. Although measurements around the knee seem to be more relevant for predictions of fracture risk in the SCI population [START_REF] Lala | Exploring the determinants of fracture risk among individuals with spinal cord injury[END_REF], the absence of both a standardised protocol available for the DXA technique and reference values such as the Tscore has limited its clinical utility.

Fragility fracture is due not only to a reduction in bone mass, but also to a specific alteration of bone geometry. However, the standard clinical DXA technique does not allow the quantification of volumetric bone density (vBMD) and bone geometry [START_REF] Nielsen | The fallacy of BMD: a critical review of the diagnostic use of dual Xray absorptiometry[END_REF] and tends to underestimate bone loss due to SCI [START_REF] Bauman | Underestimation of bone loss of the spine with posterior-anterior dual-energy X-ray absorptiometry in patients with spinal cord injury[END_REF]. Consequently, these limitations have reduced its utility in providing detailed descriptions of the components of bone structure relevant to fracture risk [START_REF] Nielsen | The fallacy of BMD: a critical review of the diagnostic use of dual Xray absorptiometry[END_REF]. Moreover, DXA does not distinguish between trabecular and cortical bone, a limitation that is particularly relevant in disuse osteoporosis due to the differences in the extent and time course of bone loss in these two components [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Ghasem-Zadeh | Heterogeneity in microstructural deterioration following spinal cord injury[END_REF][START_REF] Abdelrahman | Osteoporosis after spinal cord injury: aetiology, effects and therapeutic approaches[END_REF]. In addition, the percentage of bone loss (absolute change) in the trabecular bone response to disuse appears more pronounced and faster than in cortical bone [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Ghasem-Zadeh | Heterogeneity in microstructural deterioration following spinal cord injury[END_REF][START_REF] Abdelrahman | Osteoporosis after spinal cord injury: aetiology, effects and therapeutic approaches[END_REF]. On the other hand, peripheral quantitative computed tomography (pQCT) has unique capabilities to quantitatively assess vBMD, as well as microstructural cortical and trabecular features [START_REF] Boutroy | In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography[END_REF]. Assessment of these parameters may improve fracture risk prediction in both able-bodied men [START_REF] Sheu | Bone strength measured by peripheral quantitative computed tomography and the risk of nonvertebral fractures: the osteoporotic fractures in men (MrOS) study[END_REF] and individuals with SCI [START_REF] Lala | Exploring the determinants of fracture risk among individuals with spinal cord injury[END_REF]. However, although pQCT technique is widely used in research, it is still unsuitable for routine clinical use because it remains very expensive and the radiation dose delivered is relatively high (approximately 90 μSv) compared with DXA when scanning central sites (spine and hip) are performed [START_REF] Adams | Quantitative computed tomography[END_REF] To overcome this limitation, advanced DXA-based methods have been developed, such as the 3D modelling approach that evaluates vBMD and differentiates trabecular and cortical compartments at the proximal femur. The 3D-DXAderived parameters were demonstrated to be highly correlated with equivalent measurements assessed by quantitative computed tomography (QCT) [START_REF] Humbert | 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the Cortex in 3D from DXA images[END_REF].

Therefore, the present study was conducted in order to (i) analyse the effect of SCI on 3D-DXA parameters by comparing values between patients and able-bodied controls and (ii) model the profiles of 3D-DXA-derived parameters according to the time since injury.

Subjects and Methods

Subjects

This study followed a cross-sectional design, and the protocol has been described in detail elsewhere [START_REF] Maimoun | Periostin and sclerostin levels in individuals with spinal cord injury and their relationship with bone mass, bone turnover, fracture and osteoporosis status[END_REF]. Eighty individuals with SCI, having a neurological lesion exclusively due to trauma, were consecutively recruited from the Centre Mutualiste PROPARA, a specialised spinal cord injury clinic (Montpellier, France). According to the American Spinal Injury Association Impairment Scale (ASIA), they were classified as complete (AIS A), incomplete with no motor function below the neurological level of injury (AIS B), or incomplete with incomplete motor recovery (AIS C and D). All skeletal fractures at SCI onset and post-SCI were recorded. In most cases, post-SCI fractures were reported during the anamnesis and were confirmed by radiography in our clinic or by checking the patient records. Spasticity and its location were also recorded. All individuals with SCI used a manual or electrically assisted wheelchair as their primary means of mobility. The physical activity of the patients was not recorded, but none was participating in a training program liable to modify bone components, such as functional electrical stimulation (FES) or whole-body vibration.

The individuals with SCI were compared to a control group of 25 healthy males with similar age. None of the participants had a history of a neuromuscular disease like multiple sclerosis, had an evident secondary cause of osteoporosis (i.e. osteogenesis imperfecta or known endocrine disorders associated with bone loss), presented current bone fractures, or was taking medication known to affect bone metabolism, including bisphosphonates, within the 3 years before inclusion. They had to be 18 years or older.

Study approval was obtained from the Regional Research Ethics Committee (Comite de Protection des Personnes Sud-Mediterranee IV, Montpellier, France). Participants were given oral and written information and then delivered written consent.

Methods

All participants were evaluated in a single session after an overnight fast. Height was measured with a stadiometer. Weight was determined using a weight scale with a precision of 0.1 kg. BMI was calculated as weight (kg) divided by the square of height (m).

Bone Mineral Density, Body Fat and Fat-Free Soft Tissues

DXA (Hologic QDR-4500A, Hologic, Inc., Waltham, MA) measured the aBMD (g/cm2) at specific bone sites: the anteroposterior lumbar spine (L1-L4), the dominant arm radius, and the hip. When an individual with SCI had a fracture, osteosynthesis material or heterotopic ossification, the contralateral hip and radius were assessed. All scanning and analyses were performed by the same operator to ensure consistency after following standard quality control procedures. Quality control for DXA was checked daily by scanning a lumbar spine phantom consisting of calcium hydroxyapatite embedded in a cube of thermoplastic resin (DPA/QDR-1; Hologic x-calibre anthropometric spine phantom). The least significant change (LSC) was calculated in our Department of Nuclear Medicine (CHRU Montpellier, France) and was determined at 0.013 g/cm2 for the lumbar spine and 0.018 g/ cm2 at the hip.

DXA-Based 3D Modelling

3D-Shaper® software (version 2.2, 3D-Shaper Medical, Spain) was used to assess the trabecular macrostructure and the cortex in 3D from DXA scans [START_REF] Humbert | 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the Cortex in 3D from DXA images[END_REF]. The method relies on a statistical shape and density model of the proximal femur built from a database of QCT images. The 3D statistical model is registered onto the DXA scan to obtain a 3D patientspecific model of the patient's proximal femur shape and BMD distribution. The cortical thickness and density are computed by fitting a mathematical function to the density profile computed along the normal vector at each node of the proximal femur surface mesh [START_REF] Humbert | Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship[END_REF]. 3D-DXA measurements include the vBMD and bone mineral content (BMC) of the cortical, trabecular and integral (trabecular + cortical) bone compartments and the cortical thickness. The 3D-DXA measurements were performed at the total proximal femur, neck, trochanter and shaft (intertrochanteric) regions. The precise localisations of the 3D-DXA regions analysed by the software were previously described by Humbert et al. [START_REF] Humbert | 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the Cortex in 3D from DXA images[END_REF] and Clotet et al. [START_REF] Clotet | Structural parameters of the proximal femur by 3-dimensional Dual-energy X-ray absorptiometry software: comparison With quantitative computed tomography[END_REF]. The accuracy of the models and measurements provided by the 3D-DXA software algorithm was evaluated in a previous study by comparing 3D-DXA and QCT analyses [START_REF] Humbert | 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the Cortex in 3D from DXA images[END_REF]. As previously described in detail [START_REF] Clotet | Structural parameters of the proximal femur by 3-dimensional Dual-energy X-ray absorptiometry software: comparison With quantitative computed tomography[END_REF] for each region, the 3D-Shaper® software program computes the following variables to give an estimated strength of the femoral regions: (i) the bone crosssectional area (CSA; in square centimetres), an index of resistance to axial forces; (ii) the section modulus (Z) (cubic centimetres), an index of strength in bending computed from the crosssectional moment of inertia (iii) that weights the area in the cross-section by the square of its distance from the centroid (CSMI; in cm4); and (iv) the buckling ratio (BR), an index of susceptibility to local cortical buckling under compressive loads. Higher values are associated with greater estimated femoral strength for CSA, Z and CSMI parameters except for BR, for which higher values estimated lower femoral strength.

Assays

Blood samples (25 ml) were collected in the morning (08h30-09h30) in sterile chilled tubes by standard venepuncture technique. The samples were allowed to clot at room temperature and were then centrifuged at 2500 rpm for 10 min at 4 °C. Concerning bone metabolism, serum samples were assayed by Cobas 6000 (Roche Diagnostic, Mannheim, Germany) for osteocalcin (OC), procollagen type I N-terminal propeptide (PINP) and type I-C telopeptide breakdown products (sCTx). The inter-and intra-assay CVs for the latter three parameters were lower than 7%.

Statistical Analysis

Characteristics are described with proportions for categorical variables and with means and standard deviations (SD) for quantitative variables. Controls were compared with paraplegic and tetraplegic subgroups, as well as with the group of all individuals with SCI (paraplegics and tetraplegics). Paraplegics were also compared with tetraplegics. Comparisons between groups were performed using Student's t-test for quantitative variables and the Chi-square or Fisher's exact test for qualitative variables. In order to quantify the effect size for each quantitative variable, we calculated Cohen's d (difference between the means of two groups divided by a pooled standard deviation), which is a standardised measure of the effect of a factor (here SCI) independent of the scale of the parameter under consideration [START_REF] Cohen | Statistical Power Analysis for the Behavioral Sciences[END_REF]. As an indication, a Cohen's d value of 0.2 corresponds to a small effect of the factor, a value of 0.5 to a medium effect, a value of 0.8 to a large effect, a value of 1.2 to a very large effect and a value of 2 to a huge effect [START_REF] Sawilowsky | New effect size rules of thumb[END_REF].

We then examined the relationships between bone markers and time since injury by fitting a nonlinear segmented regression model [START_REF] Bang | Tutorial in biostatistics: Analyzing associations between total plasma homocysteine and B vitamins using optimal categorization and segmented regression[END_REF]. For each bone marker, we defined two segments for which we estimated the parameters through iteration by minimising the sum of residual squares, using the NLIN procedure in SAS software. This method allowed us to identify a breakpoint or moment, at which the two segments connect, representing the time since injury when the bone markers stopped decreasing and stabilised at the plateau phase. After estimating the breakpoint, a graphic representation of bone marker evolution was achieved through monotonic B-spline regression with one knot, using the previously estimated breakpoint as the knot. All analyses were conducted using SAS Enterprise Guide software (V.7.15, SAS Institute, Cary, NC, USA). A two sided p-value of < 0.05 was considered significant.

Results

Subject Characteristics

Patient characteristics are shown in Table 1. There was no significant difference in mean age, weight or BMI between the healthy controls and the individuals with SCI. The time from the initial injury varied from 0.3 to 49.1 years with a median of 10.8 years. Most individuals with SCI presented a complete medullar motor lesion (AIS A or B, 88.8%); 78.8% were paraplegics and 21.2% were tetraplegics. Sixty-four post-SCI fractures were counted with a post-SCI occurrence of 14 years ranging from 0.5 to 48.8 years. Fractures were mostly observed in femoral and tibial regions.

Comparison of Bone Mineral Density Between Healthy Controls and Individuals with SCI

Table 1 presents the aBMD for the two groups at various bone sites. The aBMD measurements were not available for 1 individual with SCI at the total hip, for 15 individuals at the lumbar spine and for 1 individual at the radius (Table 1). The main reasons were the presence of osteosynthesis material, osteoarthritis, vertebral fracture, heterotopic ossifications and muscle contractures. Compared with controls, individuals with SCI presented significantly (p < 0.0001) lower aBMD at the total hip (Cohen's d = -1.4), with a Z-score representing only -2.8 SD of normal values and ranging from -5.8 SD to + 1.1 SD. Lumbar spine and radius aBMD values in the individuals with SCI were similar to the values in healthy controls (Cohen'd 0.24 and -0.09, respectively). No difference between the two groups was observed for bone turnover markers of bone formation or bone resorption.

Comparison of 3D-DXA Bone Parameters Between Healthy Controls and Individuals with SCI

The comparison of 3D-DXA parameters is presented in Table 2. Individuals with SCI had significantly lower 3D-DXA-derived parameters (p < 0.001 for all) in total proximal femur and at all femoral subregions, i.e. neck, trochanter and shaft regions, compared with healthy controls. For the total proximal femur, the Cohen's d values were -1.31 for integral vBMD, -1.00 for trabecular vBMD and -1.67 for cortical vBMD. The degree of alteration was approximately the same for all three femoral subregions. In total proximal femur and all femoral subregions, the difference between the two groups tended to be greater for cortical vBMD compared with trabecular vBMD (Cohen's d around -1.6 and -1.1, respectively). The mean cortical thickness was also significantly lower (p < 0.0001) in men with SCI compared with healthy controls for total proximal femur (Cohen's d = 1.36) and for all femoral subregions (neck region excepted). Strength estimates (CSA, CSMI, Z) were lower and BR was higher in men with paraplegia, and tetraplegia indicated a fragility of the femur to withstand axial and compressive forces.

Bone Parameters According to the Time Since Injury

Figure 1 shows the variation of 3D-DXA-derived parameters at total proximal femur with the time since injury. The decreasing bone parameters tended to reach a new steady state after 5.1 years for integral vBMD, 7.4 years for trabecular vBMD and 9.2 for cortical vBMD, beyond which bone loss may continue but at a slower rate.

Bone Parameters According to the Lesion Level (paraplegia vs tetraplegia)

Data comparing the characteristics of men with paraplegia and men with tetraplegia are presented in Table 3. When the two subgroups were examined, the age and the time since injury were comparable, whereas weight and BMI were lower in the men with tetraplegia compared with paraplegia. All aBMD parameters were lower in individuals with tetraplegia than in those with paraplegia. When the data were compared with data from healthy controls, the reduced bone mass was regional: total hip aBMD involvement was universal in the two subgroups of men with SCI, despite being accentuated in those with tetraplegia, whereas radius aBMD involvement was only seen in association with tetraplegia. Last, lumbar spine aBMD was higher in persons with paraplegia compared with controls. Both subgroups presented similar normal values for markers of bone turnover (data not shown).

Concerning 3D parameters (Table 4), the total proximal femur of men with paraplegia and men with tetraplegia showed lower values of integral, cortical and trabecular vBMD and cortical thickness compared with healthy controls. With a few exceptions, these alterations were observed in all regions of the femur for the two SCI subgroups and were associated with significantly lower CSA, CSMI and Z and significantly higher BR in comparison with controls. Minor differences were observed according to the lesion level, mainly at the neck region, and characterised by lower trabecular vBMD, mean cortical thickness, CSA, CSMI and Z in tetraplegics compared with paraplegics. Figure 2 shows a 3D reconstruction of the femoral cortical thickness in representative individuals with SCI (paraplegic and tetraplegic) and in controls.

Discussion

This study confirmed that SCI induces a decrease in aBMD in the sublesional area. In addition, 3D-DXA revealed a deterioration of integral, trabecular and cortical vBMD and cortical thickness at femoral regions in these patients. These findings may explain the reduction in estimated bone strength that predisposes this population to a high risk of fragility fractures.

It has been widely shown that SCI induces an extensive alteration of aBMD, mainly at weight-bearing bone sites [START_REF] Maimoun | Changes in osteoprotegerin/RANKL system, bone mineral density, and bone biochemicals markers in patients with recent spinal cord injury[END_REF][START_REF] Maimoun | Periostin and sclerostin levels in individuals with spinal cord injury and their relationship with bone mass, bone turnover, fracture and osteoporosis status[END_REF][START_REF] Maimoun | Bone loss in spinal cord-injured patients: from physiopathology to therapy[END_REF]. Our results confirmed these findings, but additionally showed that tetraplegic patients presented a more extensive and accentuated loss of aBMD than paraplegic patients. The conventional DXA analysis used to determine the effect of SCI on bone tissue does not provide any information on the specific alteration of the trabecular and cortical bone compartments at femoral regions, although this would be useful for improving therapeutic targeting [START_REF] Winzenrieth | Effects of osteoporosis drug treatments on cortical and trabecular bone in the femur using DXA-based 3D modeling[END_REF].

The 3D-DXA approach has been extensively used to characterize vBMD in various populations [START_REF] Maimoun | Modification of bone mineral density, bone geometry and volumetric BMD in young women with obesity[END_REF][START_REF] Freitas | Cortical and trabecular bone analysis of professional dancers using 3D-DXA: a casecontrol study[END_REF][START_REF] Brance | Trabecular and cortical bone involvement in rheumatoid arthritis by DXA and DXAbased 3D modelling[END_REF]. This study is the first to compare the vBMD between men with SCI and healthy controls. Our results demonstrated that at total proximal femur, a specific pattern of bone loss occurred after SCI, which was characterised by the concomitant deterioration of the trabecular and cortical compartments leading to a lower integral vBMD in individuals with SCI compared with controls. The lower trabecular density may be explained by a reduction in the trabecular numbers, which increases the trabecular separation associated with lower trabecular thickness [START_REF] Ghasem-Zadeh | Heterogeneity in microstructural deterioration following spinal cord injury[END_REF]. It was proposed that the loss of cortical vBMD was due to an increase in cortical porosity [START_REF] Ghasem-Zadeh | Heterogeneity in microstructural deterioration following spinal cord injury[END_REF] and/or a reduction in cortical thickness due to endocortical resorption [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Dionyssiotis | Bone loss 316 L. Maïmoun et al.1 3 and mechanical properties of tibia in spinal cord injured men[END_REF][START_REF] Rittweger | Bone adaptation to altered loading after spinal cord injury: a study of bone and muscle strength[END_REF].

Nevertheless, when we analysed our data in greater detail we found that, although a large effect size was observed for the two compartments (Cohen's d > 1.2) [START_REF] Sawilowsky | New effect size rules of thumb[END_REF], cortical bone tended to be more affected by SCI (Cohen's d = -1.67) than trabecular bone (Cohen's d = -1.00) for total proximal femur. Interestingly, the magnitude differences for the two compartments between controls and individuals with SCI tended to be homogeneous for the three femoral subregions. Cortical thickness was also found to be significantly lower in SCI at the total hip, trochanter and shaft. At the neck, although lower in SCI (1.715 mm vs 1.754 mm in controls), the difference was not statistically significant. This could be explained by larger measurement errors at the neck, where the cortex is very thin, compared with other regions such as the shaft or the trochanter [START_REF] Humbert | 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the Cortex in 3D from DXA images[END_REF]. Consequently, the neurological lesion seems to have a similar detrimental effect along its proximal length. The concomitant alterations of trabecular and cortical bone density and geometry parameters (i.e. cortical thickness) may explain the estimated reduction in bone strength regarding axial and compressive forces, as indicated by the decrease in CSA, CSMI and the section modulus and the increase in the buckling ratio. The reduction in femoral bone strength that was previously reported [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Frey-Rindova | Bone mineral density in upper and lower extremities during 12 months after spinal cord injury measured by peripheral quantitative computed tomography[END_REF] may explain why fracture risk increases in this population. These findings strongly suggest that anti-resorptive treatments such as bisphosphonates should be proposed to prevent the rapid bone loss in the first months after injury [START_REF] Bauman | Effect of pamidronate administration on bone in patients with acute spinal cord injury[END_REF].

To our knowledge, only Gifre et al. [START_REF] Gifre | Analysis of the evolution of cortical and trabecular bone compartments in the proximal femur after spinal cord injury by 3D-DXA[END_REF] investigated the effect of SCI in a small group of 16 males using a similar method [START_REF] Gifre | Analysis of the evolution of cortical and trabecular bone compartments in the proximal femur after spinal cord injury by 3D-DXA[END_REF]. In the first-year post-neurological injury, a decrease in trabecular (22%) and cortical (5%) vBMD, associated with a significant decrease in cortical thickness (11%), was reported. However, it is likely that the real bone loss was underestimated because the first measure was performed 93 ± 25 days after injury, which meant that the initial acute bone loss was not taken into account [START_REF] Roberts | Longitudinal study of bone turnover after acute spinal cord injury[END_REF][START_REF] Maimoun | Changes in osteoprotegerin/RANKL system, bone mineral density, and bone biochemicals markers in patients with recent spinal cord injury[END_REF][START_REF] Maimoun | Use of bone biochemical markers with dual-energy x-ray absorptiometry for early determination of bone loss in persons with spinal cord injury[END_REF]. In addition, the lack of a control group makes it difficult to precisely determine the real impact of SCI on the 3D-Shaper® parameters. Although we should keep in mind that it is difficult to merge our results with those of Gifre et al. [START_REF] Gifre | Analysis of the evolution of cortical and trabecular bone compartments in the proximal femur after spinal cord injury by 3D-DXA[END_REF] due to the differences in the DXA device used and the population studied, the 3D-derived parameters were systematically lower in our chronic population. These results suggest that bone loss continues beyond the 1st year and can be identified with this method. However, it is difficult to precisely define the time course of bone loss in individuals with SCI because several studies have reported an early intense loss with stabilisation of bone mass at about 2 years [START_REF] Maimoun | Changes in osteoprotegerin/RANKL system, bone mineral density, and bone biochemicals markers in patients with recent spinal cord injury[END_REF][START_REF] Biering-Sorensen | Longitudinal study of bone mineral content in the lumbar spine, the forearm and the lower extremities after spinal cord injury[END_REF], whereas still others have suggested than bone mass continues to decline throughout the chronic phase [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Bauman | Continuous loss of bone during chronic immobilization: a monozygotic twin study[END_REF]. Part of this divergence may be attributed to (i) whether the study design was cross-sectional or longitudinal and whether inter-individual variations were taken into account, and (ii) the parameters evaluated by different techniques (DXA vs pQCT or HR-pQCT). Our modelling approach, which characterised the variation of integral and trabecular bone in terms of time since injury, demonstrated that new steady states tended to be reached at 5.1 and 7.3 years, respectively, and further showed a delay for cortical vBMD at 9.2 years since injury. The bone biochemical markers found within the reference values also corroborated the finding of no additional or limited bone loss in our individuals with SCI (mean time since injury: 14 years). In line with our results, Eser et al. [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF] conducted a cross-sectional study in a comparable population of SCI (men with a mean 12 years since injury) and reported that bone geometry and BMD reached new steady states at values ranging from 25 to 75% lower than the values of the reference group within 3 to 8 years after onset of SCI. The time scale depended on the bone parameters and the measured site (femur or tibia) [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF]. The same team later confirmed these findings with a longitudinal study [START_REF] Frotzler | Bone steady-state is established at reduced bone strength after spinal cord injury: a longitudinal study using peripheral quantitative computed tomography (pQCT)[END_REF].

In our study, we sought to determine whether the lesion level had an effect on the 3D parameters. Although our two subgroups of individuals with SCI presented similar characteristics in terms of age and time since injury, the tetraplegic individuals tended to present accentuated lower 3D-DXAderived parameters than the paraplegic subgroup, which confirmed some previous results [START_REF] Ghasem-Zadeh | Heterogeneity in microstructural deterioration following spinal cord injury[END_REF][START_REF] Biggin | Evaluation of bone mineral density and morphology using pQCT in children after spinal cord injury[END_REF], but not all [START_REF] Frey-Rindova | Bone mineral density in upper and lower extremities during 12 months after spinal cord injury measured by peripheral quantitative computed tomography[END_REF].

As above-mentioned, the data are very limited on the effects of SCI on cortical and trabecular compartments assessed by the same 3D-DXA method. We therefore attempted to draw a parallel with data obtained using pQCT or HR-pQCT, the gold standard technique for bone microstructural analysis. Nevertheless, due to technical constraints, only the distal or shaft regions of the tibia and femur can be analysed with these devices. This might constitute a greater bias, most likely because the proximal tibial region is more affected by loss, as estimated by aBMD or vBMD, than the femoral region following SCI [START_REF] Biering-Sorensen | Bone mineral content of the lumbar spine and lower extremities years after spinal cord lesion[END_REF][START_REF] Zheng | Bone mineral density at the distal femur and proximal tibia and related factors during the first year of spinal cord injury[END_REF][START_REF] Coupaud | Decreases in bone mineral density at cortical and trabecular sites in the tibia and femur during the first year of spinal cord injury[END_REF]. Using pQCT, McCarthy et al. [START_REF] Mccarthy | Changes in the structural and material properties of the tibia in patients with spinal cord injury[END_REF] investigated the proximal tibia of nine males with time since injury > 24 months (mean 77.1 ± 33.4 months) and found a reduction of almost 40% in trabecular BMD compared with in controls, while there were small but significant differences (~ 6%) in the cortical BMD only at the anterior aspect of the tibia. Interestingly, individuals with acute SCI (time since injury < 6 months) did not present significant alteration of either the trabecular or cortical compartment [START_REF] Mccarthy | Changes in the structural and material properties of the tibia in patients with spinal cord injury[END_REF]. Ghasem-Zadeh et al. [START_REF] Ghasem-Zadeh | Heterogeneity in microstructural deterioration following spinal cord injury[END_REF] recently used HR-pQCT and reported lower values of total (ranging from -36.6 to -30% for tetraplegics and paraplegics, respectively), cortical (-16% and -18%, respectively) and trabecular (-42% and -52%, respectively) vBMD at distal tibia in men with SCI and a mean 6.5 years since injury compared with healthy controls. In line with these findings, we calculated the percentage difference between controls and individuals with SCI (data not shown) and also observed a reduction of the same order of magnitude for total proximal femur (-28.4% for total vBMD, -14.3% for cortical vBMD and -28% for trabecular vBMD). As expected, the vBMD loss observed in our population was more pronounced than during the 1st year after injury for cortical (2.5-7%) and trabecular (15-20%) components [START_REF] Frey-Rindova | Bone mineral density in upper and lower extremities during 12 months after spinal cord injury measured by peripheral quantitative computed tomography[END_REF][START_REF] Coupaud | Decreases in bone mineral density at cortical and trabecular sites in the tibia and femur during the first year of spinal cord injury[END_REF] evaluated by pQCT at tibia or femur. The concordance between our and these previous results obtained with the reference techniques suggests the potential utility of 3D-DXA for accurate follow-up of bone status in populations with SCI. However, we must note that, although the decrease in trabecular vBMD seems to be unanimously observed [4, 17, 34-36, 42, 43], as is the reduction in cortical thickness [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Dionyssiotis | Bone loss 316 L. Maïmoun et al.1 3 and mechanical properties of tibia in spinal cord injured men[END_REF][START_REF] Rittweger | Bone adaptation to altered loading after spinal cord injury: a study of bone and muscle strength[END_REF], some studies have shown a relatively stable cortical vBMD after the injury [START_REF] Eser | Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals[END_REF][START_REF] Dionyssiotis | Bone loss 316 L. Maïmoun et al.1 3 and mechanical properties of tibia in spinal cord injured men[END_REF][START_REF] Rittweger | Bone adaptation to altered loading after spinal cord injury: a study of bone and muscle strength[END_REF][START_REF] Coupaud | Decreases in bone mineral density at cortical and trabecular sites in the tibia and femur during the first year of spinal cord injury[END_REF][START_REF] Mccarthy | Changes in the structural and material properties of the tibia in patients with spinal cord injury[END_REF].

Our study presents some limitations that should be noted. Its cross-sectional design did not allow us to follow the variation of 3D-DXA parameters in the same subjects over a long period, thus introducing inter-individual variability. However, the inclusion of patients with a wide range of times since injury and similar disease is the only method that can be used to assess the long-term consequences of SCI. Moreover, given the difficulties of recruiting from the SCI population [START_REF] Dionyssiotis | Bone loss 316 L. Maïmoun et al.1 3 and mechanical properties of tibia in spinal cord injured men[END_REF], the constitution of a large group of individuals with SCI, as well as an age-matched uninjured control group to limit the effect of age-related bone loss, may be considered a notable strength of our study. In fact, it is probable that the bone loss was independent of age because bone structure is already adapted to minimal mechanical loading and no further reduction in mechanical loading due to ageing occurs in this population [START_REF] Frotzler | Bone steady-state is established at reduced bone strength after spinal cord injury: a longitudinal study using peripheral quantitative computed tomography (pQCT)[END_REF]. Finally, despite the evidence that hip aBMD is associated with fragility fractures [START_REF] Lala | Exploring the determinants of fracture risk among individuals with spinal cord injury[END_REF], it will be necessary to determine whether the vBMD measured at the femoral proximal region is a predictive parameter of the fragility fractures that occur mostly in the distal femur and the tibial epiphyses [START_REF] Morse | Osteoporotic fractures and hospitalization risk in chronic spinal cord injury[END_REF][START_REF] Frotzler | Long-bone fractures in persons with spinal cord injury[END_REF]. This would likely improve the medical care of patients because measurement in the hip region is the standard procedure and not all clinical centres have standardised protocols for measuring aBMD around the knee [START_REF] Lala | Exploring the determinants of fracture risk among individuals with spinal cord injury[END_REF].

In summary, this study showed that SCI induces a loss of vBMD for both cortical and trabecular compartments and a decrease in cortical thickness, resulting in low estimated bone strength. A new bone steady state tended to be reached between 5 and 9 years after SCI, and bone loss intensity tended to be higher in tetraplegics than in paraplegics. The close results obtained with 3D-DXA and those previously reported with the reference techniques (pQCT and HR-pQCT) suggest that this new approach may be useful in the SCI population to follow bone loss. Nevertheless, it remains to be demonstrated whether vBMD parameters are more sensitive than aBMD to discriminate between individuals with SCI at high or low risk of a fracture.

Acknowledgements

The authors would like to express their thanks to the individuals with spinal cord injury and the controls for their participation.

Author Contributions

Declarations

Conflict of interest I certify that neither I (Laurent Maimoun) nor my co-authors (Anthony Gelis, Chris Serrand, Thibault Mura, Vincent Boudousq, Pascal de Santa-Barbara, Didier Laux, Charles Fattal, Denis Mariano-Goulart) have a conflict of interest that is relevant to the subject matter or materials included in this work. Ludovic Humbert is an employee and shareholder of 3D-Shaper Medical.

Human Rights and Informed Consent

Approval was obtained from Regional Research Ethics Committee (Comite de Protection des Personnes Sud-Mediterranee IV, Montpellier, France). All procedures used adhere to the tenets of the Declaration of Helsinki. Participants were given oral and written information and then delivered written informed consent.