Abstract
Chronic kidney disease (CKD) affects 10–15% globally and is a marked independent risk factor for cardiovascular disease. Prevalence estimations are essential for public health planning and implementation of CKD treatment strategies. This study aimed to estimate the prevalence and stages of CKD in the population-based Lolland-Falster Health Study, set in a rural provincial area with the lowest socioeconomic status in Denmark. Additionally, the study characterized participants with CKD, evaluated the overall disease recognition, including the awareness of CKD and compared it with other common conditions. Cross-sectional data were obtained from clinical examinations, biochemical analyses, and questionnaires. CKD was defined as albuminuria (urine albumin–creatinine ratio ≥30 mg/g), estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m², or by a diagnosis in the National Patient Register. Patient awareness was assessed by self-reported CKD, and overall disease recognition by either a registered hospital diagnosis or self-reported CKD. Among 16 097 adults (median age 58.6 years), CKD prevalence was 18.0% (n = 2903), with 70.1% identified by albuminuria, 28.4% by reduced eGFR, and 1.5% by a registered diagnosis alone. Of those with CKD, 98.8% had stages 1–3 (eGFR ≥30 ml/min/1.73 m²), and 1.2% had stages 4–5 (eGFR <30 ml/min/1.73 m²). Female sex, comorbidities, smoking, and low socioeconomic parameters were independently associated with CKD. Patient awareness of CKD was 4.4%, compared to >50% for hypertension and >80% for diabetes, and the overall CKD recognition (self-reported or registered diagnosis) was 7.1%. Thus, in this population-based study, CKD was highly prevalent but poorly recognized, indicating great potential for preventing CKD progression and related complications.
Additional content
Additional content an author video to accompany this article is available at: https://oup.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=308f58bc-01e6-4208-8bc2-b24900a247c5.
Introduction
Chronic kidney disease (CKD) affects over 10% of the global adult population, is becoming more prevalent and has emerged as the 11th leading cause of death [1, 2]. CKD encompasses various disorders affecting kidney structure and function and is classified according to level of glomerular filtration rate (GFR) into stages 1–5 and albuminuria category 1–3 [3]. The most common identifiable causes of CKD include diabetes and hypertension, and the escalating prevalence of these conditions, along with the aging population, contributes substantially to the overall rise in CKD [4].
Patients with CKD often have no or very few symptoms, leaving both the patients and clinicians unaware of the condition. This lack of awareness impedes timely intervention to prevent CKD progression and complications, such as cardiovascular disease (CVD) [5]. In early-stage CKD, the risk of death from CVD may even exceed the risk of progression to kidney failure [5]. Thus, early preventive measures and treatment are crucial to mitigate both CKD and CVD burden. Albuminuria is a marker for early-stage CKD, and reducing albuminuria is an effective therapeutic strategy [3]. Yet, many countries report inadequate testing for albuminuria despite its importance and ease of testing [6–8].
In Denmark, stage 3–5 CKD prevalence is approximately 5% [9, 10], but data on earlier stages, which require testing for albuminuria, are limited. Reliable prevalence estimates in different populations are essential for public health planning and early detection strategies. Most epidemiological studies, however, are based on urban populations and rely solely on register data potentially excluding many patients with CKD who are asymptomatic or not seeking healthcare [1, 6, 10–12].
Denmark is a small high-income country with a population of approximately 5.9 million. Despite having a tax-funded healthcare system with free hospital access and relative genetic homogeneity within the population, health outcomes in Denmark vary considerably by region [13]. Lolland-Falster, a rural provincial area with 103 000 residents, is considered the most socioeconomically disadvantaged area in Denmark [14], with life expectancy significantly lower than the national average [15]. The population-based Lolland-Falster Health Study (LOFUS) was therefore initiated to explore factors influencing health in this area [16].
As part of LOFUS, the present cross-sectional study investigated the epidemiology of CKD. Unlike register-based studies, this study assessed CKD prevalence by systematic examination of albuminuria and decreased estimated GFR (eGFR), covering both early and late stages. Additionally, the study characterized participants with CKD and identified individuals who were unaware of their condition or unrecognized by the healthcare system. We hypothesized that the prevalence of CKD in Lolland-Falster was >10% and that the proportion of unrecognized CKD was >50%, thus representing a substantial public health issue contributing to the health disparities in the area.
Methods
Randomly selected adults (≥18 years) were invited to participate in LOFUS together with members of their household, regardless of age [16]. However, only adult participants were included in the present study. Data were obtained between February 2016 and February 2020, during which each participant was evaluated once through comprehensive clinical and paraclinical examinations. Moreover, participants completed an electronic questionnaire covering their medical history, medication use, lifestyle factors, as well as socioeconomic and demographic factors. The participation proportion for LOFUS was 36% [17], and the study is described in detail in the published protocol [16]. Data collected for the current study were pre-planned to include measurements of plasma creatinine, glycated hemoglobin A1c (HbA1c), and urine albumin–creatinine ratio (UACR) calculated from spot-urine samples. EGFRs were calculated using the creatinine-based Chronic Kidney Disease Epidemiology Collaboration equation without race correction [18].
Population registers
Data on study participants’ contact with hospitals were collected from the Danish National Patient Register (DNPR), a nationwide hospital register providing longitudinal data on inpatient contacts since 1977 and outpatient and emergency department contacts since 1995 [19]. Both public and private hospitals in Denmark are legally required to report patient contacts to the DNPR, ensuring high level of coverage and minimal loss-to follow up. However, primary care contacts are not registered in the DNPR. Unique Central Person Register numbers are assigned to all Danish residents at birth or upon immigration, linking our study data with the DNPR [20]. International Classification of Diseases (ICD-10) codes used to identify individuals with CKD and other comorbidities are listed in the Supplementary Material.
Definition of CKD
CKD was defined in accordance with Kidney Disease: Improving Global Outcomes (KDIGO) recommendations in terms of albuminuria (UACR above 30 mg/g) or eGFR below 60 ml/min/1.73 m2 based on a single measurement of UACR and eGFR [3]. If one of the values was in the pathological range and the other was missing, the individual was classified as having CKD. In addition, individuals with a diagnosis consistent with CKD registered in the DNPR prior to their study appointment were classified as having CKD. This ensured the inclusion of individuals with CKD and normal biochemical markers, such as those with structural kidney abnormalities. Individuals with CKD were further classified by CKD stage based on eGFR measurements, in accordance with KDIGO guidelines [3]. If an individual had albuminuria but a missing value for eGFR, the stage was considered unspecified. Finally, individuals with CKD were categorized into two groups: recognized and unrecognized. Recognized CKD included those who had a registered hospital diagnosis consistent with CKD or those who were aware of having CKD (i.e. gave an affirmative answer to the question: “Do you have a kidney disease?”). Individuals with unrecognized CKD met neither of these criteria.
Definition of comorbidities
Prevalent CVD was defined as a diagnosis of CVD, either self-reported (i.e. atherosclerosis, angina pectoris, deep vein thrombosis, or taking medication for atrial fibrillation) or listed in the DNPR. Blood pressure was measured using a standardized protocol, and the last of three consecutive measurements was used for evaluation [21]. Individuals were categorized as having hypertension if they met any of the following criteria: (i) systolic blood pressure ≥140 mmHg, (ii) diastolic blood pressure ≥90 mmHg, (iii) self-reported hypertension diagnosis, (iv) self-reported use of antihypertensive medication, or (v) had a recorded hypertension diagnosis in the DNPR [22]. Diabetes was defined by one or more of the following criteria: (i) HbA1c level ≥48 mmol/mol, (ii) self-reported diabetes diagnosis, (iii) self-reported use of antidiabetic medication, or (iv) a registered diagnosis [23]. Obesity was defined as a body mass index (BMI) ≥30 kg/m2 and abdominal obesity as a waist circumference ≥88 cm for women or ≥102 cm for men [24].
Occupational status and educational level
Data on socioeconomic status were obtained from the questionnaire. The question regarding occupational status had 16 different response options. These were divided into the following four categories in the analyses: Active (employee, self-employed, combined employee and self-employed, in the military, secondary school pupil, postsecondary student, apprentice, assisting spouse, housewife/househusband), temporarily inactive (unemployed, undergoing rehabilitation, on sickness leave for 3 months or more), inactive (retired due to age, recipient of disability benefit, early retirement), and other (other). Seven different response options for educational level were divided into the following three categories for analyses: No postsecondary education, short postsecondary education (unspecified other education, one or more short courses, vocational education, short higher education for 2–3 years), and medium or long postsecondary education (medium higher education for 3–4 years, long higher education for > 4 years).
Statistical methods
Categorical variables are presented as proportions and continuous variables as means with standard deviations or medians with interquartile ranges. Since several participants were from the same household, generalized linear mixed-effects models with household as a random effect were used to account for this clustering and estimate !odds ratios (ORs) with 95% confidence intervals (CIs). All tests were two-sided, and a P-value <0.05 was considered statistically significant. Multivariable models were adjusted for the following covariates: sex, age group, smoking, abdominal obesity, diabetes, hypertension, and CVD. The software program R (ver. 4.3.3) was used for statistical analysis [25].
Results
Prevalence of CKD and comorbidities
A total of 16 142 adults participated in LOFUS, but 45 participants were excluded due to missing data for both plasma creatinine and UACR. Therefore, 16 097 participants were included in the current study. The demographic and clinical characteristics of the participants are presented in Table 1, stratified by CKD status. The median age was 58.6 years, and 53.1% of the study sample were women. Approximately 70% of the individuals participated together with one or more of their adult household members and 96.7% of all participants were tested for albuminuria. A total of 2903 individuals were identified as having CKD, corresponding to an overall prevalence of 18.0%, with a higher prevalence among women than among men (19.5% versus 16.3%). Most individuals had CKD based on elevated UACR (70.1%); of these, 6.6% had a UACR ≥300 mg/g. In addition, 18.7% of individuals had CKD based on reduced eGFR, 9.7% had both reduced eGFR and elevated UACR, and 1.5% had CKD based solely on a diagnosis in the register. In terms of CKD staging, most individuals had stage 1 or 2 (71.3%), 21.5% had stage 3a, 6% had stage 3b, and 1.2% had stage 4 or 5. The prevalence of stage 3–5 CKD in the entire study sample was 5.1%.
Table 1.
Demographic and clinical characteristics of the participants
| Total (N = 16 097) | No CKD (n = 13 194) | CKD (n = 2903) | |
|---|---|---|---|
| Female sex, n (%) | 8542 (53.1) | 6874 (52.1) | 1668 (57.5) |
| Age (years), median (IQR) | 58.6 (46.0–68.9) | 56.6 (44.5–66.9) | 67.8 (56.5–75.1) |
| Age distribution, n (%) | |||
| 18–45 | 4014 (24.9) | 3662 (27.8) | 352 (12.1) |
| 46–65 | 6913 (42.9) | 5952 (45.1) | 961 (33.1) |
| 66–85 | 5011 (31.1) | 3521 (26.7) | 1490 (51.3) |
| 85+ | 159 (1.0) | 59 (0.4) | 100 (3.4) |
| BMI category, n (%) (kg/m2) | |||
| <25 | 5808 (36.3) | 4883 (37.1) | 925 (32.2) |
| 25–29 | 6123 (38.2) | 5,040 (38.3) | 1083 (37.7) |
| ≥30 | 4089 (25.5) | 3,225 (24.5) | 864 (30.1) |
| Waist circumference (cm), mean (SD) | 95.1 (14.7) | 94.6 (14.3) | 97.3 (16.2) |
| Systolic blood pressure (mmHg), mean (SD) | 132 (19.0) | 131 (17.9) | 140 (21.8) |
| Diastolic blood pressure (mmHg), mean (SD) | 79.0 (8.6) | 78.7 (8.2) | 80.6 (10.1) |
| Plasma creatinine (µmol/l), mean (SD) | 74.7 (17.4) | 73.0 (12.9) | 82.6 (29.0) |
| UACR (mg/g), median (IQR) | 8.0 (1.0–18.0) | 7.0 (1.0–12.0) | 46.0 (32.0–85.0) |
| HbA1c (mmol/mol), mean (SD) | 37.0 (6.7) | 36.5 (5.8) | 39.6 (9.4) |
| Abdominal obesity,a n (%) | 7790 (48.4) | 6123 (46.4) | 1667 (57.4) |
| Cardiovascular disease, n (%) | 1888 (11.7) | 1279 (9.7) | 609 (21.0) |
| Diabetes, n (%) | 1072 (6.7) | 657 (5.0) | 415 (14.3) |
| Hypertension,b n (%) | 7328 (45.5) | 5316 (40.3) | 2012 (69.3) |
| Self-reported, n (%) | 4389 (27.3) | 3004 (22.8) | 1385 (47.7) |
| Blood pressure ≥140/90 mmHg, n (%) | 5145 (32.0) | 3749 (28.4) | 1396 (48.1) |
| Smoking, n (%) | |||
| Never | 6959 (46.4) | 5840 (47.5) | 1119 (41.2) |
| Former | 5324 (35.5) | 4232 (34.4) | 1092 (40.2) |
| Current | 2730 (18.2) | 2225 (18.1) | 505 (18.6) |
| Postsecondary education, n (%) | |||
| Medium/long | 3920 (25.9) | 3293 (26.5) | 627 (23.1) |
| Short | 8494 (56.1) | 6994 (56.3) | 1500 (55.1) |
| No | 2728 (18.0) | 2135 (17.2) | 593 (21.8) |
| Occupational status, n (%) | |||
| Active | 8473 (55.6) | 7510 (60.1) | 963 (34.9) |
| Temporarily not active | 539 (3.5) | 469 (3.8) | 70 (2.5) |
| Inactive | 5840 (38.3) | 4176 (33.4) | 1664 (60.3) |
| Other | 394 (2.6) | 5332 (2.7) | 62 (2.2) |
Waist circumference ≥88 cm for women and ≥102 cm for men.
Hypertension defined by either blood pressure ≥140/90 mmHg, a registered diagnosis, or a self-reported diagnoses or use of antihypertensive medication.
IQR, interquartile range; SD, standard deviation.
Compared to the non-CKD group, individuals with CKD were older and had a higher prevalence of comorbidities (Table 1). More than 30% of the individuals with CKD had obesity and 57.4% had abdominal obesity. The prevalence of diabetes in the overall study sample was 6.7% and it was three-fold more prevalent among individuals with CKD than those without. Similarly, CVD was twice as prevalent in the group with CKD than in the group without (21.0% versus 9.7%). Hypertension was observed in 45.5% of the overall study sample and 70% of these individuals presented with elevated blood pressure. In the CKD group, the prevalence of hypertension was 69.3% (Table 1).
Socioeconomic and demographic factors
The proportion of individuals with inactive occupational status was 60.3% in the CKD group and 33.4% in the non-CKD group. Most individuals in both groups had a short postsecondary education, whereas the proportion of individuals with no postsecondary education was slightly higher among those with CKD than those without.
Characteristics associated with CKD in study participants
Results of univariate and multivariable analyses are presented in Table 2. Although almost half of the individuals with CKD were younger than 65 years (Table 1), age remained a significant factor associated with CKD, producing an adjusted OR of 2.32 for individuals aged 66–85 compared to those aged 18–49. Women were more likely than men to have CKD, and this association strengthened after adjusting for age, smoking status, and comorbidities (OR, 1.52; 95% CI, 1.39–1.66). Similarly, smoking (current or former) (OR, 1.15; 95% CI, 1.06–1.26) and comorbidities such as diabetes (OR, 2.04; 95% CI, 1.76–2.36), hypertension (OR, 2.26; 95% CI, 2.04–2.51), CVD (OR, 1.40; 95% CI, 1.24–1.58), and abdominal obesity (OR, 1.10; 95% CI, 1.01–1.21) were all independently associated with CKD. Compared to having a medium or long postsecondary education, having no postsecondary education or a short postsecondary education was associated with CKD; however, the association between having a short postsecondary education and CKD became insignificant after adjusting for potential confounders. Having an inactive occupational status was associated with CKD, whereas being temporarily inactive was not.
Table 2.
Analyses of factors associated with CKD
| Univariate analysis | Multivariablea analysis | ||
|---|---|---|---|
| OR (95% CI) | OR (95% CI) | ||
| Female sex | 1.24 (1.15–1.35)*** | 1.52 (1.39–1.66)*** | |
| Age group | 18–45 years | Reference | |
| 46–65 years | 1.68 (1.48–1.92)*** | 1.12 (0.97–1.30) | |
| 66–85 years | 4.43 (3.90–5.03)*** | 2.32 (1.99–2.70)*** | |
| 86+ years | 17.96 (12.72–25.34)*** | 9.16 (6.31–13.29)*** | |
| Abdominal obesity | 1.56 (1.44–1.70)*** | 1.10 (1.01–1.21)* | |
| Cardiovascular disease | 2.49 (2.23–2.77)*** | 1.40 (1.24–1.58)*** | |
| Diabetes | 3.21 (2.81–3.67)*** | 2.04 (1.76–2.36)*** | |
| Hypertension | 3.30 (3.09–3.69)*** | 2.26 (2.04–2.51)*** | |
| Current or former smoking | 1.29 (1.19–1.41)*** | 1.15 (1.06–1.26)** | |
| Postsecondary education | Medium/long | Reference | |
| Short | 1.13 (1.02–1.25)* | 0.98 (0.88–1.10) | |
| None | 1.46 (1.29–1.66)*** | 1.18 (1.03–1.35)* | |
| Occupational status | Active | Reference | |
| Temporarily not active | 1.16 (0.90–1.51) | 1.05 (0.8–1.38) | |
| Inactive | 3.11 (2.85–3.39)*** | 1.30 (1.13–1.49)*** | |
| Other | 1.46 (1.10–1.93)*** | 1.16 (0.87–1.55) | |
P ≤ 0.001,
P ≤ 0.01,
P ≤ 0.05.
Adjusted for age, sex, abdominal obesity, diabetes, hypertension, cardiovascular disease, and smoking.
Patient awareness and overall disease recognition
Among the 2903 individuals with CKD, 4.4% (n = 128) were diagnosed with an ICD-10 code consistent with CKD in a hospital setting. A similar number (n = 129, 4.4%) of individuals were aware of having CKD (i.e. self-reported kidney disease), with the lowest levels of awareness among individuals with stage 1 and 2 (<3.0%) and the highest levels of awareness among individuals with stage 4 and 5 (67.6%). In comparison, patient awareness for hypertension (>50%) and diabetes (>80%) was markedly higher than for CKD. Over 60% of the individuals who had a registered hospital diagnosis were not aware of having CKD. Merging the group who had a diagnosis with those who were aware of having CKD yielded a combined proportion of 7.1% (n = 206) with recognized CKD (Table 3), indicating an overall proportion of unrecognized CKD that exceeded 90%. However, CKD recognition increased with advancing stages (5.4% at stages 1–3a, 22.0% at stage 3b and 67.6% at stages 4–5) and with higher CKD progression risk based on the KDIGO risk stratification (Supplementary Fig. S1). At later stages (i.e. 3–5), median age was significantly higher among those with unrecognized CKD. Compared to individuals with recognized CKD, those with unrecognized CKD had a higher mean eGFR, as well as a higher prevalence of moderately increased albuminuria (UACR = 30–299 mg/g); however, severely increased albuminuria (UACR ≥300 mg/g) was more prevalent among those with recognized than unrecognized CKD (Table 3). Abdominal obesity, CVD, hypertension, and diabetes were all more frequent in individuals with recognized CKD than in those with unrecognized CKD.
Table 3.
Participants with CKD stratified by unrecognized/recognized
| Unrecognized (n = 2697) | Recognized (n = 206) | |
|---|---|---|
| Female sex, n (%) | 1578 (58.5) | 90 (43.7) |
| Age (years), median (IQR) | 67.8 (56.4–75.0) | 68.3 (57.3–76.1) |
| Age distribution (years), n (%) | ||
| 18–45 | 332 (12.3) | 20 (9.7) |
| 46–65 | 887 (32.9) | 74 (35.9) |
| 66–85 | 1384 (51.3) | 106 (51.5) |
| 86+ | 94 (3.5) | 6 (2.9) |
| eGFR (ml/min/1.73 m2), mean (SD) | 78.7 (21.6) | 64.3 (27.9) |
| HbA1c (mmol/mol), mean (SD) | 39.4 (9.24) | 41.5 (11.5) |
| UACR (mg/g), median (IQR) | 46.0 (32.0–83.0) | 51.5 (15.3–203.0) |
| UACR category, n (%) | ||
| A1—normal to mildly increased: <30 mg/g | 475 (17.9) | 66 (33.3) |
| A2—moderately increased: 30–299 mg/g | 2036 (76.5) | 89 (44.9) |
| A3—severely increased: ≥300 mg/g | 149 (5.6) | 43 (21.7) |
| Self-reported CKD, n (%) | 0 | 129 (62.6) |
| Registered CKD diagnoses, n (%) | 0 | 128 (62.1) |
| CKD stages,a n (%) | ||
| 1: eGFR ≥90 ml/min/1.73 m2 | 880 (32.9) | 45 (22.1) |
| 2: eGFR 60–89 ml/min/1.73 m2 | 1,073 (40.1) | 57 (27.9) |
| 3a: eGFR 45–59 ml/min/1.73 m2 | 578 (21.6) | 41 (20.1) |
| 3 b: eGFR 30–44 ml/min/1.73 m2 | 135 (5.0) | 38 (18.6) |
| 4–5: eGFR <30 ml/min/1.73 m2 | 11 (0.4) | 23 (11.3) |
| Abdominal obesity, n (%) | 1,530 (56.7) | 137 (66.5) |
| Cardiovascular disease, n (%) | 546 (20.2) | 63 (30.6) |
| Diabetes, n (%) | 359 (13.3) | 56 (27.2) |
| Hypertension,b n (%) | 1,848 (68.5) | 164 (79.6) |
| Postsecondary education, n (%) | ||
| Medium/long | 588 (23.3) | 39 (19.4) |
| Short | 1389 (55.1) | 111 (55.2) |
| No | 542 (21.5) | 51 (25.4) |
| Occupational status, n (%) | ||
| Active | 902 (35.3) | 61 (30.3) |
| Temporarily not active | 65 (2.5) | 5 (2.5) |
| Inactive | 1537 (60.1) | 127 (63.2) |
| Other | 54 (2.1) | 8 (4.0) |
Not including the individuals with unspecified CKD stage.
Hypertension defined by either blood pressure ≥140/90 mmHg, a registered diagnosis, or a self-reported diagnosis or use of antihypertensive medication.
Discussion
In this population-based study, we found a CKD prevalence of 18.0%, predominantly based on the presence of moderately increased albuminuria. Over 90% of individuals with CKD were not previously hospital diagnosed or aware of their condition. The findings confirm our hypothesis that CKD represents a substantial but underrecognized public health concern in this rural provincial area of Denmark. Female sex, comorbidities including obesity, hypertension, diabetes, and CVD, as well as factors indicative of low socioeconomic status, were independently associated with CKD.
CKD prevalence
We found a high prevalence of CKD compared to several other European countries. For example, the Trøndelag Health Study (HUNT) in Norway reported a CKD prevalence of 11.1% in the 2006–2008 cohort (N > 50 000) [26]. This lower prevalence may be due to the lower mean age of the HUNT study participants compared to the participants in the LOFUS study. Moreover, the CKD prevalence in our study was twice as high as the 8.9% reported in a recent study from the Netherlands [11]. The studies differ in several aspects. In contrast to LOFUS, which studied a rural provincial population, the Dutch study focused on the general population across both urban and non-urban areas in the Netherlands, where the disease burden might be lower. The different results obtained by the two studies may also be due to the Dutch study involving less testing for albuminuria and lacking data on individuals who were not actively engaged with the healthcare system. The multinational CardioRenal and Metabolic study (CaReMe) involved 2.4 million patients with CKD and reported a pooled possible CKD prevalence of 10.0% [1]. Data in this extensive study were obtained from digital healthcare systems in 11 countries. The CaReMe study also used a similar definition of CKD to ours: either a CKD diagnosis or the presence of one pathological UACR or eGFR value was considered indicative of possible CKD, facilitating a more direct comparison between the results of these studies. However, the lower possible CKD prevalence in the CaReMe study may be partly due to fewer than 60% of the participants having albuminuria measured, compared to over 96% in LOFUS, where >70% of CKD cases were defined by increased UACR alone.
Regarding the prevalence of stage 3–5 CKD (5.1%) in our study, we noted similarities in other Scandinavian studies [6, 9, 10]. A study conducted in another region of Denmark reported a stage 3–5 prevalence of 4.8–5.0%, based exclusively on data from medical databases and used slightly different criteria to define CKD, confirming the diagnoses via a second measurement of eGFR [10]. Gasparini et al. reported a marginally higher stage 3–5 prevalence of 6.0% within a healthcare utilization cohort in Stockholm, based on at least one eGFR assessment [6].
CKD recognition
In our study, the proportion of individuals with recognized CKD was low (7.1%) but consistent with similar studies [6, 27]. Low recognition may partly reflect insufficient testing for albuminuria in high-risk groups, as reported by several studies [6–8, 10]. For example, a large study [8] from the United States reported that only 17.5% (n = 33 629) of patients with diabetes and/or hypertension had been tested, despite recommendations in the guidelines [3]. Similar to previous studies, we observed a higher proportion of recognized CKD among individuals with more severe stages [28, 29]. We also found that individuals with unrecognized CKD were older, which may be due to a perception that treatment may not enhance the quality or duration of life in this patient group, and subsequently the CKD diagnosis is not registered or communicated to the patient. The same may apply to some patients in early stages who do not yet reach the medical treatment threshold. Interestingly, we noted that over 60% of the individuals with a registered diagnosis were unaware of having CKD, suggesting that although the disease had been recognized by a physician, this had not been communicated sufficiently to the patient. A study on communication quality between CKD patients and physicians found that physicians did most of the talking, often using inadequately explained technical terms, and discussed CKD less frequently than other comorbidities [30]. Similar to findings reported by Chu et al., we found that awareness of having CKD (4.4%) was markedly lower than awareness of having diabetes (>80%) or hypertension (>50%) [28]. Clearly, improvements in CKD awareness are necessary, both at patient and policy levels, and inspiration may be derived from previous efforts to improve awareness of diabetes and hypertension.
Comorbidities and socioeconomic factors in CKD
CKD is a well-established risk factor for cardiovascular complications and mortality, even in its early stages with normal eGFR and low levels of albuminuria [31, 32]. Accordingly, more than one in five individuals with CKD had known CVD in our study, a proportion potentially underestimated [5]. Along with other cardiovascular risk factors prevalent in patients with CKD—such as diabetes, hypertension, and obesity, which our study results confirm—the individual cardiovascular risk profiles can become detrimental if action in treatment and prevention measures is not taken [33]. For instance, nearly 70% of the individuals with CKD in our study had hypertension, over half had abdominal obesity, and almost a third had obesity. In comparison, the estimated prevalence of obesity in the general Danish population was 17.5% at the time of our study, a figure projected to rise to 31.7% by 2040 [34]. The effect of obesity on CKD, independent of other cardiometabolic risk factors, remains unclear. Nonetheless, our results demonstrate a significantly and independently greater likelihood of CKD in those with abdominal obesity. This aligns with findings by Bosch et al. who observed that increased waist circumference in individuals without CKD was associated with reduced GFR and renal plasma flow, possibly due to increased renal vascular resistance [35]. In the context of the growing obesity pandemic, our results underscore the importance of recognizing obesity as a risk factor not only for CVD but also for CKD [36, 37].
Consistent with prior research our findings also establish the significant association between CKD and low educational level, especially a lack of postsecondary education [38]. In a longitudinal study, Thio et al. confirmed this association and suggested that it may be partly explained by factors such as diabetes, high BMI, smoking, hypertension, and low dietary intake of potassium [39]. Targeting these modifiable risk factors to reduce the risk of CKD among individuals with low educational levels may be feasible.
Limitations
Our study was subject to certain limitations. First, because this was a cross-sectional study, we lacked repeated measurements of eGFR and UACR over time. Consequently, there was a risk of overestimating CKD prevalence based on transient decreases in eGFR and increases in UACR [4]. However, unlike register-based studies, the samples in our study were not collected when individuals were actively engaged with the healthcare system during an acute illness that could influence eGFR and UACR measurements. Therefore, we anticipate that transient values are not a substantial bias. Second, because registered diagnoses in primary care are not included in the DNPR, there is a risk of misclassification and underestimation of the proportion of recognized CKD. However, the self-reporting of CKD in response to the questionnaire should partially counteract this problem. Finally, LOFUS had a participation proportion of 36% and may be affected by participation bias. Holmager et al. studied the participation in LOFUS and observed that non-participants were more likely to be receiving public benefits and had a three-fold higher mortality rate than participants [17]. Thus, the true prevalence of CKD in Lolland-Falster may be even higher than that recorded in our study.
Strengths
We report the largest and most comprehensive CKD prevalence study to date in Denmark, based not merely on register data but on systematic clinical and paraclinical examination of a randomly selected population. The data regarding albuminuria were almost complete, providing valuable information on the early asymptomatic stages of CKD, which is generally underrepresented in register-based studies. Moreover, previous population-based studies in Denmark were conducted on urban populations, whereas our study examines a rural provincial population. This unique feature of LOFUS enhances the generalizability of the findings by adding valuable insights from non-urban areas [17]. Although Lolland-Falster is considered a socioeconomically disadvantaged in Denmark, it is important to keep in mind that, from an international perspective, it is a part of a high-income country with universal access to free public healthcare when interpreting the results.
Clinical implications and importance of early detection
Most individuals with unrecognized CKD were identified based on the presence of moderately increased albuminuria. Therefore, it emphasizes the importance of testing for albuminuria, especially in high-risk groups such as those with diabetes, hypertension, and CVD, to timely detect individuals for whom initiation of treatment is indicated [3]. Beyond medical treatment, early recognition is indeed also essential to ensure appropriate monitoring of eGFR, UACR, and blood pressure, as well as initiation of lifestyle interventions to help prevent further disease progression and improve cardiovascular risk profiles.
Conclusions
In this large population-based study, we found that CKD was highly prevalent, but most individuals were not aware of this condition and did not carry a hospital diagnosis, highlighting a substantial but hidden public health concern. Our findings reveal a great potential for preventing CKD progression and related complications in rural provincial areas of Denmark.
Supplementary Material
Acknowledgements
LOFUS is a collaboration between Region Zealand, Zealand University Hospital, Nykøbing F., and Lolland and Guldborgsund municipalities. The authors are grateful to LOFUS for making the research data available. However, LOFUS bears no responsibility for the analysis, or the interpretation conducted within this study.
Contributor Information
Ebba Mannheimer, Department of Nephrology and Endocrinology, Rigshospitalet, Copenhagen, Denmark.
Morten Buus Jørgensen, Department of Nephrology and Endocrinology, Rigshospitalet, Copenhagen, Denmark.
Kristine Hommel, Department of Medicine, Holbæk Hospital, Holbæk, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Anne-Lise Kamper, Department of Nephrology and Endocrinology, Rigshospitalet, Copenhagen, Denmark.
Randi Jepsen, Lolland-Falster Health Study, Centre for Health Research, Zealand University Hospital, Nykøbing F, Denmark.
Knud Rasmussen, Lolland-Falster Health Study, Zealand University Hospital, Nykøbing F, Denmark.
Lau Caspar Thygesen, National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark.
Bo Feldt-Rasmussen, Department of Nephrology and Endocrinology, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Mads Hornum, Department of Nephrology and Endocrinology, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Author contributions
Conceptualization: Morten Buus Jørgensen, Kristine Hommel, Anne-Lise Kamper, Bo Feldt-Rasmussen, Mads Hornum; data curation: Ebba Mannheimer, Morten Buus Jørgensen, Randi Jepsen; formal analyses: Ebba Mannheimer, Morten Buus Jørgensen, Lau Caspar Thygesen; funding acquisition: Mads Hornum, Ebba Mannheimer; investigation: Randi Jepsen, Knud Rasmussen; project administration: Ebba Mannheimer, Morten Buus Jørgensen, Bo Feldt-Rasmussen, Mads Hornum; resources: Randi Jepsen, Knud Rasmussen; supervision: Morten Buus Jørgensen, Kristine Hommel, Anne-Lise Kamper, Bo Feldt-Rasmussen, Mads Hornum; writing—original draft: Ebba Mannheimer. All authors reviewed and edited the final manuscript.
Supplementary data
Supplementary data are available at EURPUB online.
Conflict of interest: M.H. reports honoraria for advisory boards for Astra Zeneca, Bayer, Boeringer Ingelheim, Vifor, GSK, and Novo Nordisk A/S and education for Astra Zeneca, Boeringer Ingelheim and Novo Nordisk A/S outside the scope of this manuscript. E.M. reports research grants from the Danish Kidney Association, the Danish Society of Nephrology and Helen and Ejnar Bjørnow Foundation. The remaining authors declare no conflict of interest.
Funding
This study was funded by grants from the Augustinus Foundation, the Danish Kidney Association, the Danish Society of Nephrology and the Helen and Ejnar Bjørnow Foundation. The funding source had no involvement in the study design, data collection, analysis, or interpretation, writing the report, or the decision to submit the paper for publication.
Data availability
Data from third parties are not publicly available but can be requested from LOFUS, Statistics Denmark and the Danish Health Data Authority, in compliance with Danish legislation.
Ethics
All participants in LOFUS provided written informed consent. LOFUS has been approved by the Region of Zealand’s Ethical Committee on Health Research (SJ-421) and is registered with ClinicalTrials.gov (NCT02482896). Both LOFUS and the current study are registered with the Danish Data Protection Agency (P-2024-16360 and P-788-2022).
Key points.
CKD is a major cause of morbidity and mortality and an independent risk factor for cardiovascular disease, even in early stages.
Investigating CKD epidemiology in different populations is essential for public health planning of early prevention and treatment strategies.
In this rural provincial area in Denmark, CKD prevalence was 18.0%, and over 90% were not previously hospital diagnosed or aware of their condition.
Most individuals were in the early CKD stages and identified by albuminuria, underscoring the importance of screening for albuminuria, not only reduced eGFR, in individuals at high risk of CKD.
References
- 1. Sundström J, Bodegard J, Bollmann A et al. ; CaReMe CKD Investigators. 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 2022;20:100438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Naghavi M, Ong KL, Aali A et al. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2100–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int 2024;105:S117–S314. [DOI] [PubMed] [Google Scholar]
- 4. Bikbov B, Purcell CA, Levey AS et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2020;395:709–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Gansevoort RT, Correa-Rotter R, Hemmelgarn BR et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet 2013;382:339–52. [DOI] [PubMed] [Google Scholar]
- 6. Gasparini A, Evans M, Coresh J et al. Prevalence and recognition of chronic kidney disease in Stockholm healthcare. Nephrol Dial Transplant 2016;31:2086–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Tangri N, Moriyama T, Schneider MP et al. Prevalence of undiagnosed stage 3 chronic kidney disease in France, Germany, Italy, Japan and the USA: results from the multinational observational REVEAL-CKD study. BMJ Open 2023;13:E067386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Chu CD, Xia F, Du Y et al. Estimated prevalence and testing for albuminuria in US adults at risk for chronic kidney disease. JAMA Netw Open 2023;6:E2326230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Vestergaard SV, Heide-Jørgensen U, Birn H et al. Effect of the refitted race-free eGFR formula on the CKD prevalence and mortality in the Danish population. Clin J Am Soc Nephrol 2022;17:426–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Kampmann JD, Heaf JG, Mogensen CB et al. Prevalence and incidence of chronic kidney disease stage 3-5 - results from KidDiCo. BMC Nephrol 2023;24:17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Vervloet MG, de Jong HJ, Pander J et al. Prevalence of chronic kidney disease in the Netherlands and its cardiovascular and renal complications. BMC Nephrol 2023;24:337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2011) 2022;12:7–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Holmager TLF, Lophaven SN, Mortensen LH et al. Selective migration and mortality by economic status in Lolland-Falster, Denmark, 1992–2018. Sci Rep 2022;12:19970. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Indenrigs- og sundhedsministeriet. Kommunal Udligning og Generelle Tilskud 2020. 2019. https://www.ism.dk/Media/638169825276651989/309537_kommunal_udligning_2020_final-a.pdf (7 December 2023, date last accessed).
- 15. Statistics Denmark. https://www.statistikbanken.dk/statbank5a/default.asp?w=1920 (28 October 2024, date last accessed).
- 16. Jepsen R, Egholm CL, Brodersen J et al. Lolland-Falster Health Study: study protocol for a household-based prospective cohort study. Scand J Public Health 2020;48:382–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lucia T, Holmager F, Napolitano GM et al. Health and participation in the Lolland-Falster Health Study: a cohort study. BMJ Public Health 2023;1:421. [Google Scholar]
- 18. Levey AS, Stevens LA, Schmid CH et al. ; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Lynge E, Sandegaard JL, Rebolj M. The Danish National Patient Register. Scand J Public Health 2011;39:30–3. [DOI] [PubMed] [Google Scholar]
- 20. Pedersen CB. The Danish civil registration system. Scand J Public Health 2011;39:22–5. [DOI] [PubMed] [Google Scholar]
- 21. Hansen CS, Christensen MMB, Vistisen D et al. Normative data on measures of cardiovascular autonomic neuropathy and the effect of pretest conditions in a large Danish non-diabetic CVD-free population from the Lolland-Falster Health Study. Clin Auton Res 2024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Williams B, Mancia G, Spiering W et al. ; Authors/Task Force Members. 2018 ESC/ESH Guidelines for the management of arterial hypertension: the Task Force for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension: the Task Force for the management of arterial h. J Hypertens 2018;36:1953–2041. [DOI] [PubMed] [Google Scholar]
- 23. Kristensen JK, Videbæk J, Madsen GK et al. Type 2-diabetes opfølgning og behandling. Vol. 3. udgave, Dansk Selskab for almen af medicin, Danish Association for General Practice, Clinical Guideline. 2019. p. 29. https://www.dsam.dk/vejledninger/type2/diagnosen.
- 24. Nishida C, Ko GT, Kumanyika S. Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO Expert Consultation on Waist Circumference and Waist-Hip Ratio. Eur J Clin Nutr 2010;64:2–5. [DOI] [PubMed] [Google Scholar]
- 25. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. R: The R Project for Statistical Computing. 2024. https://www.r-project.org/
- 26. Hallan SI, Øvrehus MA, Romundstad S et al. Long-term trends in the prevalence of chronic kidney disease and the influence of cardiovascular risk factors in Norway. Kidney Int 2016;90:665–73. [DOI] [PubMed] [Google Scholar]
- 27. Van den Wyngaert I, Mamouris P, Vaes B et al. An exploration of under-registration of chronic kidney disease stages 3-5 in Belgian general practices using logistic regression. PLoS One 2022;17:E0279291. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Chu CD, McCulloch CE, Banerjee T et al. ; Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team. CKD awareness among US adults by future risk of kidney failure. Am J Kidney Dis 2020;76:174–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Chu CD, Chen MH, McCulloch CE et al. Patient awareness of CKD: a systematic review and meta-analysis of patient-oriented questions and study setting. Kidney Med 2021;3:576–85.e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Powe R, Boulware LE. Quality of patient-physician discussions about CKD in primary care: a cross-sectional study. Am J Kidney Dis 2015;57:583–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Matsushita K, van der Velde M, Astor BC et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010;375:2073–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Klausen K, Borch-Johnsen K, Feldt-Rasmussen B et al. Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes. Circulation 2004;110:32–5. [DOI] [PubMed] [Google Scholar]
- 33. Rossing P, Hansen TW, Kümler T. Cardiovascular and non-renal complications of chronic kidney disease: managing risk. Diabetes Obes Metab 2024;26 Suppl 6:13–21. [DOI] [PubMed] [Google Scholar]
- 34. Tolstrup JS, Curtis AB, Petersen CB. Andelen af danskere med overvaegt, fremskrevet til 2040 Notat Statens Institut for Folkesundhed. 2024. www.sdu.dk/sif (30 December 2024, date last accessed).
- 35. Bosch A, Kannenkeril D, Ott C et al. Relation between waist circumference and the renal hemodynamic in healthy individuals. Nutr Metab Cardiovasc Dis 2024;34:755–62. [DOI] [PubMed] [Google Scholar]
- 36. Kreiner FF, Schytz PA, Heerspink HJL et al. Obesity-related kidney disease: current understanding and future perspectives. Biomedicines 2023;11:2498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. ISN-KDIGO. CKD Early Identification & Intervention Toolkit. Early Screening Booklet. https://kdigo.org/wp-content/uploads/2019/01/ISN_KDIGO_EarlyScreeningBooklet_WEB.pdf (28 October 2024, date last accessed).
- 38. Zeng X, Liu J, Tao S et al. Associations between socioeconomic status and chronic kidney disease: a meta-analysis. J Epidemiol Community Health 2018;72:270–9. [DOI] [PubMed] [Google Scholar]
- 39. Thio CHL, Vart P, Kieneker LM et al. Educational level and risk of chronic kidney disease: longitudinal data from the PREVEND study. Nephrol Dial Transplant 2020;35:1211–8. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data from third parties are not publicly available but can be requested from LOFUS, Statistics Denmark and the Danish Health Data Authority, in compliance with Danish legislation.