Chronic kidney disease progression in patients with previous type 2 diabetes and/or hypertension: a population-based cohort study from primary care in Spain

Abstract

Objectives

To evaluate whether between hypertension and type 2 diabetes (T2D)—established drivers of chronic kidney disease (CKD) progression—one might be more strongly associated with CKD progression than the other.

Design

Cohort study using a primary care database (electronic health records).

Setting

Primary care in Catalonia, Spain.

Participants

438 273 patients with CKD identified from the Information System for Research in Primary Care database in Catalonia (2007–2017) and stratified into four mutually exclusive groups based on the presence/absence of hypertension and/or T2D. Distribution of the CKD study cohort was as follows: CKD with hypertension (51.1%), CKD with T2D (3.9%), CKD with hypertension and T2D (32.8%), CKD without hypertension and T2D (12.2%).

Primary and secondary outcome measures

Patients were followed up to identify the occurrence of severe kidney impairment (SKI) and kidney failure (kidney replacement therapy/estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m²). Subdistributional hazard ratios (sHRs) were estimated using Cox regression adjusted for confounders.

Results

Compared with the CKD without hypertension and T2D group, adjusted sHRs (95% CIs) for SKI/kidney failure were 1.77 (1.65 to 1.89) for CKD with hypertension and T2D, 1.50 (1.41 to 1.59) for CKD with hypertension and 1.21 (1.09 to 1.34) for CKD with T2D, and for kidney failure were 1.24 (1.10 to 1.39) for CKD with hypertension, 0.74 (0.61 to 0.90) for CKD with T2D and 1.09 (0.96 to 1.24) for CKD with hypertension and T2D. The strongest risk factors for CKD progression were low eGFR and albuminuria, even at mild-moderate levels.

Conclusions

Hypertension could be associated with an equal/greater risk of CKD progression as T2D. Efforts to slow CKD progression should target both patients with hypertension and T2D, focusing on the identification, close monitoring and effective management of albuminuria and reduced eGFR.

Correspondence to Dr Betlem Salvador-González; [email protected]

STRENGTHS AND LIMITATIONS OF THIS STUDY

• Our study included a large population-based cohort of adults with stage I–IV chronic kidney disease (CKD) from real-world clinical practice had a long-follow-up and we adjusted for a wide range of potential confounders.

• Another strength is that we used estimated glomerular filtration rate <15 mL/min/1.73 m², in addition to kidney replacement therapy, to identify cases of kidney failure because it is not uncommon (especially for older patients) to reject the latter.

• Our findings are representative of people with CKD from Catalonia but not necessarily of people in other geographical regions.

• Some misclassification of study variables could have occurred due to possible recording inaccuracies and data incompleteness issues.

• Residual confounding cannot be excluded due to unknown or unmeasurable confounders.

Introduction

Chronic kidney disease (CKD) is a significant and increasing global public health issue.^{1 2} In 2016, the estimated worldwide CKD prevalence was approximately 4%, and the age-standardised rate of death due to CKD was 18.3 per 100 000 persons, representing almost twofold increases over the previous three decades.³ Prevalence is even greater in high- and middle-income countries, where approximately 1 in 10 people has CKD,^{4 5} with socioeconomically disadvantaged groups being disproportionately affected.⁴ Although only a small proportion of people progress to kidney failure, its prevalence is rising. Further, kidney replacement therapy (KRT; dialysis or renal transplant) is burdensome for patients and has major costs implications for healthcare systems.^{6 7}

Diabetes and hypertension are the leading aetiologic causes, as well as risk factors, for CKD onset in high- and middle-income countries.^{4 8 9} These two major conditions are common in patients with CKD, with a reported prevalence of 70–86% for hypertension, and 32–48% for diabetes.^{5 10–13} Moreover, they frequently coexist^{10 11 14} and are established drivers of CKD progression and kidney failure.^15–20 There is a literature gap with respect to the extent of potential prognostic differences by these two important comorbidities in people with CKD. Understanding whether one is associated with worse prognosis than the other would help identify the best prevention strategy for the individual. Most people with CKD have mild-to-moderate disease at diagnosis and are largely managed within primary care through risk factor management.^{21 22} Therefore, using primary care data from Catalonia, Spain, we aimed to evaluate the association between having a previous diagnosis of hypertension and/or type 2 diabetes (T2D) and the risk of progression to CKD stage 4 (severe kidney impairment (SKI)) and kidney failure in patients with incident mild-to-moderate CKD.

Methods

The current analysis was part of the Prognosis of Chronic Kidney Disease: a population-based epidemiological study (KIDNEES) study, as described previously.¹⁰ Briefly, KIDNEES was a large population-based epidemiological study of patients with CKD identified in primary care. The data source for KIDNEES was electronic health records (EHRs) from the Information System for Research in Primary Care (SIDIAP) database, which captures data from primary care centres belonging to the Institut Català de la Salut, the main provider in Catalonia. The database contains the EHRs of 5.8 million people (75% of the Catalan population), who are representative of the general population of Catalonia in terms of age, sex and geographic distribution.²³ SIDIAP had been used previously for research on CKD progression.²⁴

Patient and public involvement

Neither patients nor the public were involved in the design, conduct, reporting or dissemination plans of this research.

Study population and identification of the incident chronic kidney disease cohort

A flowchart depicting identification of the incident CKD study cohort is shown in online supplemental figure 1. Individuals aged 18–90 years were eligible for inclusion on meeting any of the following criteria between 1 January 2007 and 31 December 2017: (a) consecutive estimated glomerular filtration rate (eGFR) measurements of <60 mL/min/1.73 m² recorded more than 90 days apart, (b) consecutive abnormal albuminuria records separated more than 90 days (urine albumin-to-creatinine ratio (UACR) ≥ 30 mg/g (3.4 g/mmol) or albumin excretion rate of either ≥20 mg/L or ≥30 mg/24 hours), (c) CKD ICD-10 codes (online supplemental table 1). Cohort entry was the date of first diagnosis code, or the confirmation date in (a) or (b). Where applicable, eGFR was estimated from recorded creatinine values using the CKD-Epidemiology Collaboration formula without race correction.²⁵ Individuals with type 1 diabetes (ICD-10 code E10) or with a code for kidney failure/end-stage kidney disease (ESKD), KRT or eGFR <30 mL/min/1.73 m² at baseline were excluded.

Ascertainment of type 2 diabetes and hypertension

We classified patients into four mutually exclusive groups by previous evidence of hypertension and/or T2D before cohort entry: T2D only (CKD with T2D), hypertension only (CKD with hypertension), both hypertension and T2D (CKD with hypertension and T2D), and neither T2D nor hypertension (CKD without hypertension and T2D). Briefly, T2D was identified according to an algorithm using ICD-10 codes, treatment patterns, age at diagnosis and laboratory values (two fasting plasma glucose values ≥126 mg/dL (7.0 mmol/L) or glycated haemoglobin (HbA1c) ≥6.5%). Hypertension was based on ICD-10 codes, or mean blood pressure on two consecutive measurements with systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg separated by at least 1 week. Further details can be found in the online supplemental methods and supplemental figure 2.

Outcomes and follow-up

Study outcomes were (a) SKI/kidney failure and (b) kidney failure. Individuals were followed from the date of cohort entry (index date) until the date of the outcome, the date the patient transferred out of the contributing practice, death or the end of the study period (31 December 2018), whichever came first. Individuals with SKI during follow-up were identified by ICD-10 codes for CKD stage 4 and/or consecutive eGFR measurements of <29 mL/min/1.73 m² during a minimum 90-day period (without kidney failure). We identified individuals with kidney failure by the presence of an ICD-10 code entry for CKD stage 5/kidney failure/ESKD/KRT and/or consecutive eGFR measurements of <15 mL/min/1.73 m² during a minimum 90-day period (see online supplemental table 2 for further details). Deaths during follow-up were determined from administrative registers.

Patient variables

We obtained data on sociodemographics (age, sex and Mortality in Small Spanish Areas and Socioeconomic and Environmental Inequalities (MEDEA) deprivation index quintile),²⁶ cardiovascular risk factors, comorbidities, clinical and laboratory measurements, and use of commonly used medications associated with effects on the renal/cardiovascular systems (see online supplemental table 2 for details).

Statistical analysis

Baseline characteristics were summarised using frequency counts and percentages/medians with IQR. Group differences were analysed using χ² or Kruskal-Wallis tests. Crude incidence rates were calculated by dividing the number of incident cases by person-years’ follow-up, expressed per 1000 person-years. Kaplan-Meier curves for time to study outcomes were generated for each group. Using bivariate analysis, we investigated the proportion of patients progressing to SKI/kidney failure or kidney failure, and death according to CKD group and other characteristics. Fine and Gray proportional hazards models were used to estimate subdistributional hazard ratios (sHRs) for each outcome considering death as a competing risk, adjusting for confounders. A backward stepwise variable selection process based on Akaike’s information criterion was conducted to obtain the final models. Multiple imputation of missing values of albuminuria ascertainment and other co-variables was performed according to the Markov Chains Multiple Imputation procedure—10 imputations, 10 iterations; interactions were included in the process. Imputed variables were explored to check for quality of the imputation process. In sensitivity analyses for SRI, we first repeated the multivariate model under a complete case premise, second restricted the analysis to patients with a previous diagnosis of hypertension and third restricted the analysis to patients with T2D (with or without hypertension). Analyses were performed using R V.4.1.2 (R Foundation for Statistical Computing, Vienna, Austria) (see the Supplementary Methods for the specific packages used).

Results

A total of 438 273 patients with incident mild–moderate CKD were included: median age 75 years (IQR 66–81), 53% female, CKD with hypertension (51.1%), CKD with T2D (3.9%), CKD with hypertension and T2D (32.8%) and CKD without hypertension and T2D (12.2%). Baseline characteristics of the whole cohort and the four groups are shown in online supplemental table 3. The CKD with hypertension group was generally older (median age 76 years, IQR 68–82) and mostly female (57.1%). Patients in the CKD with T2D group were generally younger (median age 70 years, IQR 58–79), with a higher proportion of men (63.3%), and smokers (22.9%) than the other cohorts. Creatinine measurements were recorded for 96.4% of the cohort; albuminuria measurements were recorded for 62.7%, being more frequent in patients with T2D: 83.9% (CKD with hypertension and T2D), 78.9% (CKD with T2D), 55.1% (CKD with hypertension) and 32.6% (CKD without hypertension and T2D). 74% of the cohort had eGFR <60 mL/min/1.73 m² (reaching 82.1% in the CKD with hypertension group), and 38% had albuminuria (most commonly in the CKD with T2D group at 60.2%) (table 1).

Table 1

Baseline renal parameters at baseline in the KIDNEES incident CKD cohort (2007–2017) with baseline eGFR >30 mL/min/1.73 m², according to the presence of hypertension and T2D (n=438 273)

	CKD without hypertension and T2D	CKD with hypertension	CKD with T2D	CKD with hypertension and T2D	P value
eGFR category (mL/min/1.73 m2)	11.6	17.1	9.8	13.4	<0.001†
30 to <45	67.7	65.0	39.9	48.1
45 to <60	11.4	12.1	23.2	24.4
60 to <90	9.2	5.7	27.1	14.2
≥90
Albuminuria category					<0.001†
A1	73.2	70.0	39.8	48.1
A2	23.5	27.1	54.5	45.6
A3	3.3	2.9	5.7	6.4

Notes: Data are n (%) or median (IQR) as appropriate.

AUC, area under the curve; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; KIDNEES, Prognosis of Chronic Kidney Disease: a population-based epidemiological study; T2D, type 2 diabetes.

Over a mean 5.7 years’ follow-up, crude incidence rates were 8.5 per 1000 person-years for SKI, 2.3 per 1000 person-years for kidney failure and 48.5 per 1000 person-years for all-cause mortality (table 2). For SKI/kidney failure and kidney failure, rates were highest in the CKD with hypertension and T2D group (12.53 and 2.97 per 1000 person-years, respectively), followed by the CKD with hypertension group (9.16 and 2.06 per 1000 person-years, respectively). From around 4 years’ follow-up, differences between all CKD groups were seen for SKI/kidney failure, yet differences were only seen between the hypertension and T2D and other CKD groups for kidney failure (figures 1 and 2). Mortality rates were highest in the CKD with T2D group and CKD with hypertension and T2D group (63.1 and 54.7 per 1000 person-years, respectively). Event rates (cumulative incidence functions) for SKI/kidney failure versus death and for kidney failure versus death are shown in online supplemental figure 3A,B.

Figure 1. Kaplan-Meier plot for SKI/kidney failure during follow-up among the KIDNEES incident CKD cohort (2007-2017) with baseline eGFR >30 mL/min/1.73 m 2 , according to the presence of hypertension and type 2 diabetes (n=438 273). Note: Patients were censored at the earliest of the following: date of transfer out of the contributing practice, death or the end of the study period (31 December 2018). CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; KIDNEES, Prognosis of Chronic Kidney Disease: a population-based epidemiological study; SKI, severe kidney impairment; T2D, type 2 diabetes.

Figure 2. Kaplan-Meier plot for kidney failure during follow-up among the KIDNEES incident CKD cohort (2007-2017) with baseline eGFR >30 mL/min/1.73 m 2 , according to the presence of hypertension and type 2 diabetes (n=438 273). Note: Patients were censored at the earliest of the following: date of transfer out of the contributing practice, death or the end of the study period (31 December 2018). CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; KIDNEES, Prognosis of Chronic Kidney Disease: a population-based epidemiological study; T2D, type 2 diabetes.

Table 2

Crude rates for progression to SKI, kidney failure and death among the KIDNEES incident CKD cohort (2007–2017) with baseline eGFR >30 mL/min/1.73 m², according to the presence of hypertension and T2D (n=437 591)

	SKI/kidney failure		Kidney failure		Death
	Mean follow-up (years)	Crude incidence rate per 1000 person-years	Mean follow-up (years)	Crude incidence rate per 1000 person-years	Mean follow-up (years)	Crude mortality rate per 1000 person-years
Total CKD cohort	5.6	9.69	5.7	2.31	5.7	48.47
CKD group
Without hypertension and T2D	5.3	5.22	5.3	1.81	5.4	45.52
With hypertension	5.8	9.16	5.9	2.06	6.0	44.40
With T2D	4.9	6.89	5.0	1.78	5.0	63.07
With hypertension and T2D	5.4	12.53	5.6	2.97	5.6	54.72

CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; KIDNEES, Prognosis of Chronic Kidney Disease: a population-based epidemiological study; SKI, severe kidney impairment; T2D, type 2 diabetes.

Bivariate associations between patient characteristics and SKI/kidney failure and kidney failure occurrence are shown in online supplemental tables 4–7. Results of the multivariate analyses following adjustment for CKD stage and other confounders are shown in table 3 and online supplemental table 8 for SKI/kidney failure, and table 4 and online supplemental table 9 for kidney failure. Compared with the CKD without hypertension and T2D group, the hazard of progression to SKI/kidney failure was highest in the CKD with hypertension and T2D group (sHR 1.77, 95% CI 1.65 to 1.89), followed by the CKD with hypertension group (sHR 1.50, 95% CI 1.41 to 1.59) and CKD with T2D group (sHR 1.21, 95% CI 1.09 to 1.34). Lower eGFR and higher albuminuria at baseline were the main independent risk factors for progression to SKI/kidney failure. Additionally, strong independent associations with SKI/kidney failure were found for age, anaemia and uncontrolled systolic blood pressure. Estimates from sensitivity analyses were broadly consistent with those from the main analysis (online supplemental tables 10–12). Compared with the CKD without hypertension and T2D group, the adjusted hazard of progression to kidney failure was higher in the CKD with hypertension group (sHR 1.24, 95% CI 1.10 to 1.39), lower in the CKD with T2D group (sHR 0.74, 95% CI 0.61 to 0.90) and unchanged in the CKD with hypertension and T2D group. Similar to SKI/kidney failure, lower eGFR and higher albuminuria were the strongest risk factors for progression to kidney failure. Notable was the markedly higher hazard with elevated UACR (sHR 4.0, 95% CI 3.5 to 4.6 for category A2 vs A1, and sHR 15.5, 95% CI 13.9 to 17.3 for category A3 vs A1), and a less strong effect (although still high) with lower eGFR stages. We observed an increased risk of progression to kidney failure among men (sHR 1.32, 95% CI 1.24 to 1.40), and a stronger reduced risk with increasing age (sHR 0.14, 95% CI 0.13 to 0.16 for age ≥80 years vs <65 years).

Table 3

Multivariate adjusted sHR for SKI/kidney failure at follow-up considering death as a competing risk, with imputed data in the KIDNEES cohort with baseline estimated glomerular filtration rate >30 mL/min/1.73 m² (n=438 273)

	Adjusted sHR (95% CI)*	P value
CKD group
Without hypertension and T2D	1.0 (reference)
With hypertension	1.50 (1.41 to 1.59)	<0.001
With T2D	1.21 (1.09 to 1.34)	<0.001
With hypertension and T2D	1.77 (1.65 to 1.89)	<0.001
Age (years)
<65	1.0 (reference)
65 to 80	0.76 (0.73 to 0.79)	<0.001
≥80	0.66 (0.63 to 0.69)	<0.001
Sex (reference female)
Female	1.0 (reference)
Male	0.97 (0.94 to 1.00)	0.057
eGFR (mL/min/1.73 m2)
>90	0.35 (0.31 to 0.39)	<0.001
60 to <90	1.0 (reference)
45 to <60	2.72 (2.55 to 2.91)	<0.001
30 to <45	10.55 (9.85 to 11.29)	<0.001
Albuminuria category (reference A1)
A1	1.0 (reference)
A2	2.10 (1.88 to 2.33)	<0.001
A3	5.71 (5.34 to 6.12)	<0.001

*Adjusted for all the other variables in the table plus MEDEA deprivation index, smoking status, BMI, Charlson comorbidity score, and the comorbidities and medications in online supplemental table 8.

CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; KIDNEES, Prognosis of Chronic Kidney Disease: a population-based epidemiological study; MEDEA, Mortality in Small Spanish Areas and Socioeconomic and Environmental Inequalities; sHR, subdistributional hazard ratio; SKI, severe kidney failure; T2D, type 2 diabetes.

Table 4

Multivariate adjusted subdistributional HR for kidney failure considering death as a competing risk, with imputed data and variables selection process in the KIDNEES cohort with baseline eGFR >30 mL/min/1.73 m² (n=438 273)

	Adjusted sHR (95% CI)*	P value
CKD group	1.0 (reference)
Without hypertension and T2D	1.24 (1.10 to 1.39)	<0.001
With hypertension	0.74 (0.61 to 0.90)	0.002
With T2D	1.09 (0.96 to 1.24)	0.179
With hypertension and T2D
Age (years)	1.0 (reference)
<65	0.36 (0.34 to 0.39)	<0.001
65 to 80	0.14 (0.13 to 0.16)	<0.001
≥80
Sex (reference female)	1.0 (reference)
Female	1.32 (1.24 to 1.40)	<0.001
Male
eGFR (mL/min/1.73 m2)	0.38 (0.33 to 0.44)	<0.001
>90	1.0 (reference)
60 to <90	2.73 (2.47 to 3.01)	<0.001
45 to <60	7.94 (7.11 to 8.86)	<0.001
30 to <45
Albuminuria category (reference A1)	1.0 (reference)
A1	4.03 (3.53 to 4.61)	<0.001
A2	15.51 (13.92 to 17.28)	<0.001
A3	1.0 (reference)

*Adjusted for all the other variables in the table plus MEDEA deprivation index, smoking status, BMI, Charlson comorbidity score, and the comorbidities and medications in online supplemental table 9.

CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; KIDNEES, Prognosis of Chronic Kidney Disease: a population-based epidemiological study; MEDEA, Mortality in Small Spanish Areas and Socioeconomic and Environmental Inequalities; sHR, subdistributional hazard ratio; SKI, severe kidney failure; T2D, type 2 diabetes.

Discussion

In this population-based cohort of patients with CKD, when compared with patients without hypertension or T2D, the risk of progressing to SKI/kidney failure was highest in those with both conditions, followed by those with hypertension and those with T2D. These findings were confirmed after adjusting for eGFR, albuminuria, presence of hypertension and T2D complications, blood pressure, HbA1c control and other covariates. For kidney failure, a higher risk was only found among patients with hypertension (without T2D), with a lower risk seen for those with T2D (without hypertension).

We are aware of one other study that has directly compared renal progression according to diabetes/hypertension,²⁷ and, to our knowledge, ours is the first set in primary care. In Italy, de Nicola et al²⁷ followed 729 patients with CKD stages I–IV treated in a specialised renal clinic and evaluated renal progression according to underlying kidney disease. Patients with diabetic kidney disease demonstrated a faster rate of eGFR decline compared with those with hypertensive kidney disease and had an almost twofold higher risk of the renal composite of kidney failure plus eGFR decline ≥40%. Additionally, control of blood pressure and especially proteinuria greatly reduced the risks. Koye and colleagues²⁸ found that in patients with diabetes, the increased risk of kidney failure/CKD progression among those with reduced eGFR was much lower when albuminuria/proteinuria was absent. In Nichols et al,²⁰ eGFR decline was generally larger among patients with T2D, particularly those with severely increased albuminuria, but the risk of progression was substantially higher with increasing albuminuria among patients either with or without T2D. Differences between findings from de Nicola et al and our study warrant explanation. We considered T2D and hypertension as underlying conditions rather than causal diagnoses and adjusted for blood pressure and HbA1c control and albuminuria—the most remarkable risk factors in patients with CKD and diabetes. Moreover, the classical presentation of CKD in T2D is changing, which in turn can change evolution to kidney failure.²⁹ In our cohort, 40% of the CKD with T2D group had normal/mildly increased albuminuria with a lower risk of progression. All-cause mortality was higher in the CKD with T2D group (despite being younger, possibly preventing them reaching kidney failure). Further, patients with T2D could progress to advanced CKD stages at an earlier age, thereby increasing the likelihood of receiving KRT—the outcome commonly measured in other studies, which could have overidentified leading to an overestimation of risk. Our findings that low eGFR and albuminuria, even at mild-moderate levels, are the strongest risk factors for CKD progression (SKI and kidney failure), are consistent with previous literature.^{18 30–35}

Diabetes is the main cause of KRT and has received much attention. According to the present results, compared with eGFR and albuminuria, the residual role of T2D and hypertension in CKD progression is much smaller. Efforts to effectively manage patients with CKD in primary care should focus on the clinical features of CKD, especially albuminuria in both T2D and hypertension. This should include proper assessment of CKD—a third of our CKD cohort did not have a baseline albumin measurement recorded, and this proportion was larger when T2D was absent, in accordance with similar studies.^{5 36} In a recent meta-analysis, albuminuria screening was undertaken in 35% of patients with diabetes and 4% in those with hypertension (without diabetes).³⁷ Clinical practice guidelines recommend the use of ACE inhibitors and Angiotension receptor blockers (ARBs) in people with albuminuria independently on blood pressure⁸ yet research suggests this is suboptimal.^{5 36} Recently, SGLT2i' with 'sodium-glucose cotransporter-2 inhibitors (SGLT2is) and aldosterone antagonists have been shown to reduce albuminuria in patients previously treated with ACE inhibitors/ARBs.^38–40 Despite the number of patients with severe albuminuria being small, benefits from potential reduction in kidney failure would be clinically meaningful. Other primary care management factors that could help reduce progression to kidney failure include encouraging smoking cessation, and adequate control of systolic blood pressure and HbA1c levels.

Strengths of our study include the large population-based cohort of adults with stage I–IV CKD from real-world clinical practice, adjustment for a wide range of potential confounders, and the long follow-up (appropriate when studying conditions with slow progression). Unlike several other studies, we used eGFR <15 mL/min/1.73 m², in addition to KRT, to identify cases of kidney failure because it is not uncommon (especially for older patients) to reject the latter.⁴¹ This could have captured more elderly patients than in other studies, and while these patients often have a more benign prognosis, the presence of kidney failure can worsen quality of life and comorbidity management.⁸ Our study also has limitations. Our findings are representative of the Catalonia demographic³ but not necessarily of people in other regions. We did not use the 'Kidney Disease Improving Global Outcomes glomerular filtration rate/albuminuria categories (KDIGO GA) risk categories but rather the separate eGFR and ACR categories. As our findings showed a different distribution of renal parameters across the four CKD groups, we believe that the assessment of separate eGFR and ACR categories allowed for a better assessment of their individual weight on the study outcomes. We are aware of potential heterogeneity of the CKD without hypertension and T2D reference group yet believe it does not affect the relationships between CKD groups. It is also noteworthy that the prevalence of albuminuria in the CKD with hypertension and CKD with T2D groups were broadly similar to that observed from the CKD Prognosis Consortium.³⁷ Additionally, some CKD patients with hypertension might not have been identified because 21% of people in our CKD cohort without identified hypertension did not have a recorded blood pressure measurement. This could have led to a biased estimation of effects, diminishing the real differences between CKD patients with detected hypertension and those classified as without hypertension. This scenario would not have applied to T2D where only 4.3% of people without a T2D diagnosis according to the study criteria had no glycaemia measurement, and overall, we believe that the perfect identification of hypertension and T2D would produce higher differences between these groups. Some misclassification could have occurred due to possible recording inaccuracies and data incompleteness issues. In terms of exposure variables, any misclassification due to coding inaccuracies would have been non-differential, thereby underestimating the strength of the observed associations. Last, residual confounding cannot be excluded due to unknown or unmeasurable confounders.

In conclusion, our results suggest that in patients with CKD, hypertension could be associated with an equal or even greater risk of disease progression than T2D. Efforts to slow CKD progression should focus on the identification, close monitoring and effective management of albuminuria and reduced eGFR, targeting patients with both T2D and hypertension.

We thank Susan Bromley (EpiMed Communications) for medical writing assistance funded by Bayer AG in accordance with Good Publication Practice. The authors thank the Departament de Recerca i Universitats de la Generalitat de Catalunya to the Research Group (‘Malaltia, risc cardiovascular i estils de vida en atenció primària (MARCEVAP)’, Code 2021 SGR 01543) for the support received.

Data availability statement

Data are available upon reasonable request. Data are available from the corresponding author upon reasonable request.

Ethics statements

Patient consent for publication

Not applicable.

Ethics approval

Ethical approval of the study protocol was obtained from the IDIAPJGol Clinical Research Ethics Committee (19/082-P).

Footnote

Contributors OCP, DV and BS-G conceived the study. All authors were involved in the design of the study. OCP performed the data analysis. All authors interpreted the results, reviewed manuscript drafts and approved the final version of the manuscript. BS-G is the guarantor.

Funding This study was funded by Bayer AG. The sponsor was involved in the study design and the interpretation of the results, but not in the collection or analysis or data. The sponsor was not involved in the writing of the report or the decision to submit the paper for publication, apart from the role of David Vizcaya (author) who is employed by Bayer. Ultimate responsibility for opinions, interpretations and conclusions lies with the authors.

Competing interests OCP works for IDIAPJGol and BS-G, OCP, SCG, JRS, DBL and AAR develop their research work within the IDIAPJGol, which has received research funding from Bayer AG for this study. DV is an employee of Bayer.

Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

References

¹ Jha V, Garcia-Garcia G, Iseki K, et al. Chronic kidney disease: global dimension and perspectives. Lancet 2013; 382: 260–72. doi:10.1016/S0140-6736(13)60687-X

² Xie Y, Bowe B, Mokdad AH, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int 2018; 94: 567–81. doi:10.1016/j.kint.2018.04.011

³ García-Gil MDM, Hermosilla E, Prieto-Alhambra D, et al. Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP). Inform Prim Care 2011; 19: 135–45. doi:10.14236/jhi.v19i3.806

⁴ Webster AC, Nagler EV, Morton RL, et al. Chronic Kidney Disease. Lancet 2017; 389: 1238–52. doi:10.1016/S0140-6736(16)32064-5

⁵ Sundström J, Bodegard J, Bollmann A, et al. Prevalence, outcomes, and cost of chronic kidney disease in a contemporary population of 2·4 million patients from 11 countries: The CaReMe CKD study. Lancet Reg Health Eur 2022; 20: 100438. doi:10.1016/j.lanepe.2022.100438

⁶ Saran R, Robinson B, Abbott KC, et al. US Renal Data System 2018 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis 2019; 73: A7–8. doi:10.1053/j.ajkd.2019.01.001

⁷ Kerr M, Bray B, Medcalf J, et al. Estimating the financial cost of chronic kidney disease to the NHS in England. Nephrol Dial Transplant 2012; 27 Suppl 3: iii73–80. doi:10.1093/ndt/gfs269

⁸ Kidney Disease Improving Global Outcomes. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int Suppl 2013; 3: 4.

⁹ Botdorf J, Chaudhary K, Whaley-Connell A. Hypertension in Cardiovascular and Kidney Disease. Cardiorenal Med 2011; 1: 183–92. doi:10.1159/000329927

¹⁰ Cunillera-Puértolas O, Vizcaya D, Cerain-Herrero MJ, et al. Cardiovascular events and mortality in chronic kidney disease in primary care patients with previous type 2 diabetes and/or hypertension. A population-based epidemiological study (KIDNEES). BMC Nephrol 2022; 23: 376. doi:10.1186/s12882-022-02966-6

¹¹ Kovesdy CP, Isaman D, Petruski-Ivleva N, et al. Chronic kidney disease progression among patients with type 2 diabetes identified in US administrative claims: a population cohort study. Clin Kidney J 2021; 14: 1657–64. doi:10.1093/ckj/sfaa200

¹² Go AS, Yang J, Tan TC, et al. Contemporary rates and predictors of fast progression of chronic kidney disease in adults with and without diabetes mellitus. BMC Nephrol 2018; 19: 146. doi:10.1186/s12882-018-0942-1

¹³ Major RW, Shepherd D, Medcalf JF, et al. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study. PLoS Med 2019; 16: e1002955. doi:10.1371/journal.pmed.1002955

¹⁴ Rao MV, Qiu Y, Wang C, et al. Hypertension and CKD: Kidney Early Evaluation Program (KEEP) and National Health and Nutrition Examination Survey (NHANES), 1999-2004. Am J Kidney Dis 2008; 51: S30–7. doi:10.1053/j.ajkd.2007.12.012

¹⁵ Zhang X, Fang Y, Zou Z, et al. Risk Factors for Progression of CKD with and without Diabetes. J Diabetes Res 2022; 2022: 9613062. doi:10.1155/2022/9613062

¹⁶ Shardlow A, McIntyre NJ, Fluck RJ, et al. Chronic Kidney Disease in Primary Care: Outcomes after Five Years in a Prospective Cohort Study. PLoS Med 2016; 13: e1002128. doi:10.1371/journal.pmed.1002128

¹⁷ Tozawa M, Iseki K, Iseki C, et al. Blood pressure predicts risk of developing end-stage renal disease in men and women. Hypertension 2003; 41: 1341–5. doi:10.1161/01.HYP.0000069699.92349.8C

¹⁸ Vejakama P, Ingsathit A, Attia J, et al. Epidemiological study of chronic kidney disease progression: a large-scale population-based cohort study. Medicine (Balt) 2015; 94: e475. doi:10.1097/MD.0000000000000475

¹⁹ Hsu C, Iribarren C, McCulloch CE, et al. Risk factors for end-stage renal disease: 25-year follow-up. Arch Intern Med 2009; 169: 342–50. doi:10.1001/archinternmed.2008.605

²⁰ Nichols GA, Déruaz-Luyet A, Brodovicz KG, et al. Kidney disease progression and all-cause mortality across estimated glomerular filtration rate and albuminuria categories among patients with vs. without type 2 diabetes. BMC Nephrol 2020; 21: 167. doi:10.1186/s12882-020-01792-y

²¹ Nihat A, de Lusignan S, Thomas N, et al. What drives quality improvement in chronic kidney disease (CKD) in primary care: process evaluation of the Quality Improvement in Chronic Kidney Disease (QICKD) trial. BMJ Open 2016; 6: e008480. doi:10.1136/bmjopen-2015-008480

²² Maycock AJ, O’Callaghan CA. The role of primary care in managing chronic kidney disease. Prescriber 2016; 27: 34–7. doi:10.1002/psb.1460

²³ Recalde M, Rodríguez C, Burn E, et al. Data Resource Profile: The Information System for Research in Primary Care (SIDIAP). Int J Epidemiol 2022; 51: e324–36. doi:10.1093/ije/dyac068

²⁴ Robinson DE, Ali MS, Pallares N, et al. Safety of Oral Bisphosphonates in Moderate-to-Severe Chronic Kidney Disease: A Binational Cohort Analysis. J Bone Miner Res 2021; 36: 820–32. doi:10.1002/jbmr.4235

²⁵ Levey AS, Stevens LA, Schmid CH, et al. A New Equation to Estimate Glomerular Filtration Rate. Ann Intern Med 2009; 150: 604. doi:10.7326/0003-4819-150-9-200905050-00006

²⁶ Felícitas Domínguez-Berjón M, Borrell C, Cano-Serral G, et al. Construcción de un índice de privación a partir de datos censales en grandes ciudades españolas (Proyecto MEDEA). Gac Sanit 2008; 22: 179–87. doi:10.1157/13123961

²⁷ De Nicola L, Provenzano M, Chiodini P, et al. Independent Role of Underlying Kidney Disease on Renal Prognosis of Patients with Chronic Kidney Disease under Nephrology Care. PLoS One 2015; 10: e0127071. doi:10.1371/journal.pone.0127071

²⁸ Koye DN, Magliano DJ, Reid CM, et al. Risk of Progression of Nonalbuminuric CKD to End-Stage Kidney Disease in People With Diabetes: The CRIC (Chronic Renal Insufficiency Cohort) Study. Am J Kidney Dis 2018; 72: 653–61. doi:10.1053/j.ajkd.2018.02.364

²⁹ Afkarian M, Zelnick LR, Hall YN, et al. Clinical Manifestations of Kidney Disease Among US Adults With Diabetes, 1988-2014. JAMA 2016; 316: 602–10. doi:10.1001/jama.2016.10924

³⁰ Astor BC, Matsushita K, Gansevoort RT, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int 2011; 79: 1331–40. doi:10.1038/ki.2010.550

³¹ Reichel H, Zee J, Tu C, et al. Chronic kidney disease progression and mortality risk profiles in Germany: results from the Chronic Kidney Disease Outcomes and Practice Patterns Study. Nephrol Dial Transplant 2020; 35: 803–10. doi:10.1093/ndt/gfz260

³² Minutolo R, Lapi F, Chiodini P, et al. Risk of ESRD and death in patients with CKD not referred to a nephrologist: a 7-year prospective study. Clin J Am Soc Nephrol 2014; 9: 1586–93. doi:10.2215/CJN.10481013

³³ Yang W, Xie D, Anderson AH, et al. Association of kidney disease outcomes with risk factors for CKD: findings from the Chronic Renal Insufficiency Cohort (CRIC) study. Am J Kidney Dis 2014; 63: 236–43. doi:10.1053/j.ajkd.2013.08.028

³⁴ Dalrymple LS, Katz R, Kestenbaum B, et al. Chronic kidney disease and the risk of end-stage renal disease versus death. J Gen Intern Med 2011; 26: 379–85. doi:10.1007/s11606-010-1511-x

³⁵ Eriksen BO, Ingebretsen OC. The progression of chronic kidney disease: a 10-year population-based study of the effects of gender and age. Kidney Int 2006; 69: 375–82. doi:10.1038/sj.ki.5000058

³⁶ Bello AK, Ronksley PE, Tangri N, et al. Quality of Chronic Kidney Disease Management in Canadian Primary Care. JAMA Netw Open 2019; 2: e1910704. doi:10.1001/jamanetworkopen.2019.10704

³⁷ Shin J-I, Chang AR, Grams ME, et al. Albuminuria Testing in Hypertension and Diabetes: An Individual-Participant Data Meta-Analysis in a Global Consortium. Hypertension 2021; 78: 1042–52. doi:10.1161/HYPERTENSIONAHA.121.17323

³⁸ Agarwal R, Filippatos G, Pitt B, et al. Cardiovascular and kidney outcomes with finerenone in patients with type 2 diabetes and chronic kidney disease: the FIDELITY pooled analysis. Eur Heart J 2022; 43: 474–84. doi:10.1093/eurheartj/ehab777

³⁹ Bakris GL, Agarwal R, Anker SD, et al. Effect of Finerenone on Chronic Kidney Disease Outcomes in Type 2 Diabetes. N Engl J Med 2020; 383: 2219–29. doi:10.1056/NEJMoa2025845

⁴⁰ Mosenzon O, Wiviott SD, Heerspink HJL, et al. The Effect of Dapagliflozin on Albuminuria in DECLARE-TIMI 58. Diabetes Care 2021; 44: 1805–15. doi:10.2337/dc21-0076

⁴¹ Hemmelgarn BR, James MT, Manns BJ, et al. Rates of treated and untreated kidney failure in older vs younger adults. JAMA 2012; 307: 2507–15. doi:10.1001/jama.2012.6455