Ulises Leal1, Nelina Alejandra Ruiz-Fernández2, Lisbeth Loaiza3, Milagros Espinoza 4 .

1 Médico Cirujano. Esp. en Medicina Interna. Ambulatorio Urbano de San Diego. Estado Carabobo, Venezuela. Unidad de Atención Médico Integral de la Universidad de Carabobo (UAMI). Valencia, Venezuela. ORCID:

2 Lic. en Bioanálisis. Dra. en Ciencias Fisiológicas. Dpto de Morfofisiopatología, Esc. de Bioanálisis Sede Carabobo. Fac. de Ciencias de la Salud. Universidad de Carabobo. Valencia, Venezuela. Instituto de Investigaciones en Nutrición (INVESNUT). Fac. de Ciencias de la Salud. Universidad de Carabobo. Valencia, Venezuela. ORCID: . Email de contacto:

3 Médico Cirujano. Dra. en Patología Existencial. Dpto. de Parasitología, Esc. de Ciencias Biomédicas y Tecnológicas. Facultad de Ciencias de la Salud. Universidad de Carabobo. Valencia, Venezuela.

4 Lic. en Bioanálisis. Dra. en Gerencia. Dpto. de Investigación y Desarrollo Profesional, Esc. de Bioanálisis Sede Carabobo. Fac. de Ciencias de la Salud. Universidad de Carabobo. Valencia, Venezuela. Centro de Investigaciones Médicas y Biotecnológicas de la Universidad de Carabobo (CIMBUC). Fac. de Ciencias de la Salud. Valencia, Venezuela. ORCID:

Key concepts:

Chronic kidney disease is defined by glomerular filtration rates below 60 mL/min/1.73m2. This condition is associated with the joint occurence of several cardiovascular risk factors known as metabolic syndrome.

Mild decreases in glomerular filtration rates (60-89 mL/min/1.73m2), increases (hyperfiltration) and proteinuria (indicative of kidney damage) are not habitually considered to be associated to metabolic syndrome. It is known that such disturbances lead to greater risk of cardiovascular mortality and predict the development of terminal kidney disease.

There is little data on such associations in developing countries. This work sets out to cover this gap in knowledge, assessing whether adults who present with metabolic syndrome or some of its symptoms are at greater risk of having altered glomerular filtration rates and proteinuria.

Our findings support the claim that it is important to detect and provide early treatment for metabolic syndrome, as a way of preserving glomerular function.


Introduction: The relation between metabolic syndrome (MS) and its components with glomerular filtration rates and proteinuria are not yet widely understood. The main goal of this work was to associate estimated glomerular filtration rates (eGFR) and proteinuria to MS and its separate components in adults with cardiometabolic risk factors who sought medical attention at a public health centre located at San Diego, Carabobo State, Venezuela. Methods: Descriptive transversal study (n=176 patients). We measured weight, height, waist circumference, body fat percentage and arterial pressure, and tested for blood sugar, creatinine, urea, ureic nitrogen, total cholesterol, LDLc, HDLc, tryglicerides, total blood A1C glycosylated hemoglobin, and proteinuria in partial urine samples. We estimated eGFR using an equation and computed BMI scores. Results: the observed MS frequency was higher in patients with chronic kidney disease (eGFR < 60 mL/min/m 2), slightly reduced gGFR (60-89 mL/min/m2), hyperflitration or proteinuria. We found a statistically significant association between mild reductions in glomerular filtration rates and MS, high glycemia, and low HDL cholesterol, which was observed with and without adjusting for sex, age and BMI. When adjusting for glycemic control, the association between metabolic syndrome, mild reductions in eGFR and proteinuria persisted. Hyperfiltration risk was not associated with metabolic syndrome. Conclusion: reductions in eGFR and proteinuria were associated with MS and its individual components. Other studies are necessary to confirm these findings.

Keywords: metabolic syndrome; glomerular filtration rates; proteinuria .


Introduction: The relationship of the metabolic syndrome (MS) and its components with the reduced glomerular filtration rate and proteinuria is not yet widely elucidated. The aim of the study was to associate the estimated glomerular filtration rate (eGFR) and proteinuria to MS and its individual components in adults with cardiometabolic risk factors, who attended a public health center in the municipality of San Diego, Carabobo State, Venezuela. Methods: Descriptive and cross-sectional study (n=176 individuals). Weight, height, waist circumference, body fat percentage and blood pressure were measured; serum glucose, creatinine, urea, ureic nitrogen, total cholesterol, low (LDLc) and high (HDLc) density lipoprotein cholesterol, triglycerides and glycosylated hemoglobin A1C in whole blood were determined; the presence of proteinuria was determined in partial urine. The eGFR was estimated by equation and the body mass index (BMI) was calculated. Results: The frequency of MS was significantly higher among patients with CKD (eGFR < 60 mL/min/m2), mildly reduced eGFR (60-89 mL/min/m2), hyperfiltration or proteinuria. The risks of mildly reduced eGFR and protenuria were significantly associated with elevated fasting blood glucose, low HDLc and MS, with and without adjustment for sex, age and BMI. When adjusted additionally for glycemic control, the risks of mildly reduced eGFR and proteinuria remained associated with MS. The risk of hyperfiltration was not associated with MS. Conclusion: The reduction in estimated glomerular function and proteinuria were associated with MS and its individual components. Other studies that confirm the results are required.

Keywords : metabolic syndrome; glomerular filtration rate; proteinuria.


Introdução: A relação da síndrome metabólica (SM) e seus componentes com a taxa de filtração glomerular e proteinúria ainda não está amplamente elucidada. O objetivo foi associar a taxa de filtração glomerular estimada (TFGe) e proteinúria com a SM e seus componentes individuais em adultos com fatores de risco cardiometabólico, que atendidos em um centro de saúde pública localizada no município San Diego, Carabobo -Venezuela.Métodos: Estudo descritivo-transversal (n = 176 pacientes). Peso, altura, circunferência da cintura, percentual de gordura corporal e pressão arterial foram medidos; foram determinada em soro glicose, creatinina, ureia, azoto da ureia, colesterol total, colesterol de lipoproteína de baixa (LDLc) e alta densidade (HDLc), triglicerídeos e hemoglobina glicada A1C em sangue total; a presença de proteinúria foi determinada em urina parcial,. A TFG foi estimada por equação e o índice de massa corporal (IMC) foi calculado.Resultados: A frequência de SM foi maior em pacientes com doença renal crônica (TFGe <60 mL/min/m2), TFGe discretamente diminuída (60-89 mL/min/m2), hiperfiltração ou proteinúria. Os riscos de uma ligeira diminuição na TFGe e proteinúria foram significativamente associados à SM, glicose sérica alta e HDLc baixo, com e sem ajuste para sexo, idade e IMC. Quando ajustado adicionalmente para o controle glicêmico, os riscos de eGFR e proteinúria levemente diminuídos permaneceram associados à SM. O risco de hiperfiltração não foi associado à SM.Conclusão: A redução de TGFe e proteinúria foram associaram à SM e seus componentes individuais. Outros estudos devem confirmar os resultados.

Palavras chave: síndrome metabólica; taxa de filtração glomerular; proteinuria


Chronic kidney disease (CKD) 1 is defined as kidney damage or reduced glomerular filtration rates (eGFR below 60 mL/min/1.73m 2 during more than three months); proteinuria is one of its symptoms. It constitutes a worldwide healthcare issue, whose prevalence is estimated to be around 8% and 16%. Moreover, the prevalence of terminal kidney disease is around 500 to 1000 individuals per million 2. In the year 2013, chronic kidney insufficiency and other kidney diseases were the third cause of death in Venezuela 3.

Cardiovascular disease is the main cause of death in patients with CKD, even during early stages without observable vascular involvement, which is not only due to habitual risk factors for cardiovascular disease, but includes several non-habitual risk factors 4. Metabolic syndrome (MS) is defined as the simultaneous presentation of varying cardiometabolic risk factors (both habitual and non-habitual), such as abdominal obesity, altered glycemia, insulin resistance, atherogenic dyslipidemia (hypertrygliceridemia and low LDL cholesterol) and high blood pressure 5.

MS and its indiviudal components are associated with an elvated risk of chronic kidney disease 6. However, evidence on the association between mild reductions in glomerular filtration rates (eGFR: 60-89 mL/min/1.73m 2) and MS is not yet entirely unambiguous 7,8, and has not been thoroughly explored either. The same holds for the association between hyperfiltration and increases in eGFR and MS 9, 10. The number of studies on this topic carried out in developing countries is low, and since racial differences affect the incidence of metabolic syndrome and chronic kideny disease, the actual association for a given population might differ from the one observed when studying other populations.

The study of disturbances in glomerular filtration rates below the treshold established for chronic kidney disease is relevant for primary healthcare. Mild reductions in glomerular function increase the risk of cardiovascular mortality 11 and predict the development of kidney disease 12. On the other hand, hyperfiltration 13 and proteinuria 14 (values ≥1+ in urine) have been associated with higher mortality by all causes.

The main goal of the present work was to associate estimated glomerular filtration rates and proteinuria with metabolic syndrome and its individual components in adult men and women with cardiometabolic risk factors who sought attention at CAREMT in a public healthcare center in San Diego, Carabobo, Venezuela.


Descriptive, transversal study carried out in a public primary healthcare center in San Diego, Carabobo, Venezuela. Our study population is composed by all adult patients (patients aged 18-65, any sex) who sought attention at CAREMT (Cardio R enal Endócrino Metabólico y Tabaco, Heart, Kidneys, Endocryne System, Metabolism and Smoking), a division dedicated to screening and providing care to patients with cardiometabolic risk factors.

Our sample consisted of 210 patients. We used non-probabilistic intentional sampling, excluding individuals with a prior diagnosis of kidney failure, or who received the diagnosis during planned evaluations that were part of this study (rGFR < 15 mL/min/1.73m 2), and those who presented with any of the following: extreme BMI (< 19 kg/m² or > 35 kg/m²), significant disturbances in muscle mass (amputations, mass muscle loss, muscle diseases or paralysis), pregnancy, severe liver disease, generalized edema, ascites, signs of current infections, and malignant or autoimmune disease. Sampling also excluded patients with a history of cardiovascular events and patients from which the required antropometric measures could not be obtained. After applying exclusion criteria, the sample included 176 patients.

All patients who were included in our study signed an informed consent form, and our research protocol adhered to the standards established by the Helsinki Declaration (2013 version). Data collection started with a questionnaire aimed to collect data on variables of interest such as sex, age, smoking habits, familial or personal history of diabetes, cerebrovascular disease and ischemic cardiomyopathy, among other comorbidities and risk factors.

We determined each patient's weight, height, waist circumference (measured as the circumference along a line exactly halfway between the lower ribs and the edge of the iliac crest) and arterial pressure following standard protocols. Blood pressure was measured using an OMRON M7 sphygmomanometer (Omron Health Care, Kyoto, Japan), validated following the guidelines in 15. We further measured body fat percentages by electric bioimpedance 15 using a body composition analyzer (model TBF 300 A Tanita ®) and computed BMI scores [weight(kg)/height(m)2], which were classified based on definitions by the WHO 16.

We extracted venous blood samples from patients (12 hours of fasting), which were used to determine serum levels of sugar, creatinine, urea, ureic nitrogen, total cholesterol, LDLc, HDLc, and tryglicerides. Creatinine was measured using a commercial kit implementing an automated and optimized two-point cynetic method (Creatinina K Labtest ®), which was calibrated using SRM 914 from NIST (National Institute of Standards and Technology), and takes results traceable to IDMS (isotope dilution, mass spectrometry). The remaining data were collected using automated enzymatic colorimetric methods. Glycosylated hemoglobin A1 C (HbA1C) quantities were measured with a complete blood test by immunoassay. Additionally, we requested a first morning urine partial sample to measure the amount of protein using a reactive strip.

We obtained an estimated glomerular filtration rate by applying the equation CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) 17, for which we used the renal function calculator available at . Based on the classification of kidney damage stages proposed by the National Kidney Foundation (K/DOQI)18, we defined normal eGFR as ≥ 90 mL/min/1.73m2, mild reductions in eGFR as values between 60 and 89 mL/min/1.73m2, chronic kidney disease as eGFR < 60 mL/min/1.73m2; we defined hyperfiltration as eGFR < 90 mL/min/1.73m2. Considering that there is still no consensus over the definition of hyperfiltration, it was established that every value above the 95th percentile specific to a given sex and age group (we split the sample into two groups based on the median age). Observing at least one protein cross (+) was established as a sufficient criteria for proteinuria.

The presence of metabolic syndrome and the categorization of its individual components was established based on a unified definition 5; we defined high blood pressure 19 and diabetes 20 as glycemia ≥ 126mg/dL and/or HbA1C > 6.5%), in accordance with international criteria.

Statistical Analysis

We used the statistical software SPSS version 20. Data analysis was carried out using descriptive statistics (median, standard deviations, median and interquartile range, absolute and relative frequencies) and variable normals were estimated using the Kolmogorov-Smirnov test. We applied the unpaired t-test or Mann-Whitney U test, depending on the case, to compare continuous variables based on sex and the presence of metabolic syndrome. Chi2 was used to assess the association between proteinuria and glomerular function categories the presence of metabolic syndrome and its components. Logistic regression allowed us to estimate odds ratios (OR) for hypofiltration, mild reductions in eGFR, hyperfiltration and proteinuria in association with metabolic syndrome, its individual components, and the number of metabolic syndrome components. Odds ratios were calculated with and without adjusting for sex, age, BMI and HbA1 C.


Women represented 53% of the sample under study. More than one third of the sample reported a familial history of cardiovascular disease (36.9%), while only 5.7% reported smoking; 53.4% and 22.2% were respectively overweight or obese (based on BMI); 29.5% were diabetic and 43.8% had high blood pressure; 42% were under hypotensive treatment when the study was conducted.

The median age for both sexes was 52, but for women it was significantly higher. Median values for weight, height, waist circumference and creatinine, as well as the frequency of diabetes, were significantly higher in men. Body fat percentages, the frequency of proteinuria, metabolic syndrome, high waist circumference and low HDLc were higher in women. Disturbances in eGFR had a significant association with sex, with reductions below 60 mL/min/1.73m2 being more common for women (Table 1). Minimal and maximal values of eGFR in the total group were between 45 and 128 mL/min/1.73m 2.

Table N°1: Characteristics of Study Participants by Sex .


Whole sample

n = 176

n = 94

n = 82



52.0 (47.25-56.0)

54.0 (48.0-57.0)

51 (45.8-55.0)


Weight (kg)

69.0 (63-76)

66.0 (59.0-73.0)

72.5 (66.8-80.2)

< 0.001

Height (m2)

1.58 (1.52-1.64)

1.56 (1.50-1.60)

1.64 (1.56-1.70)

< 0.001

BMI (kg/m2)

27.3 (25.0-29.7)

27.5 (24.6-29.0)

27.2 (25.1-30.0)


WC (cm)

89.0 (83.2-97.0)

89.0 (82.8-92.2)

92.5 (84.0-98.2)


Body Fat (%)

37.4 (32.2-39.8)

38.5 (35.0-41.8)

35.0 (27.3-38.6)

< 0.001

Blood Pressure (mmHg)

120.0 (110.0-139.5)

120.0 (110.0-140.0)

120.0 (110.0-137.2)


PAD (mmHg)

71.5 (70.0-80.0)

77.0 (70.0-80.0)

70.0 (70.0-80.0)


Glucose (mg/dL)

98.0 (89.0-120.75)

98.0 (88.8-111.8)

101.0 (89.0-140.8)


Creatinine (mg/dL)

0.9 (0.8-1.1)

0.9 (0.8-1.0)

1.0 (0.9-1.1)

< 0.001

Urea (mg/dL)

23.0 (20.0-26.0)

23.0 (20.0-27.7)

22.0 (20.0-25.0)


BUN (mg/dL)

10.7 (9.3-12.1)

10.7 (9.3-12.9)

10.3 (9.3-11.6)


CT (mg/dL)

197.5 (173.0-233.0)

202.0 (174.5-239.5)

197.0 (166.8-230.5)


LDLc (mg/dL)

123.9 (91.8-151.2)

123.1 (94.2-154.2)

125.3 (90.8-140.8)


HDLc (mg/dL)

43.0 (38.0-51.0)

43.0 (38.0-50.2)

43.0 (38.8-52.0)


TGL (mg/dL)

167.5 (115.2-210)

163.0 (114.0-220.0)

168.0 (116.8-204.0)


HbA1C (%)

5.7 (4.5-6.8)

5.5 (4.5-6.5)

5.9 (4.3-7.3)


Excess Weight n (%)

133 (75.6)

68 (72.3)

65 (79.3)


Diabetes n (%)

52 (29.5)

21 (22.3)

31 (37.8)


HTA n (%)

77 (43.8)

43 (45.7)

34 (41.5)


Urine Protein with one or more + n (%)

123 (69.9)

73 (77.7)

50 (61.0)


eGFR (ml/min/1.73m2)





GFR Categories n (%)


21 (11.9)

18 (19.1)

1 (1.2)

< 0.001

Mild reduction

90 (51.1)

48 (51.1)

44 (53.7)


61 (34.7)

25 (26.6)

34 (41.5)


4 (2.3)

3 (3.2)

3 (3.7)

SMet n (%)

108 (61.4)

65 (69.1)

43 (52.4)


Individual MS components n (%)

High CC

123 (69.9)

77 (81.9)

49 (56.1)

< 0.001

High Blood Pressure

80 (45.5)

46 (48.9)

34 (41.5)


Low HDLc

97 (55.1)

68 (72.3)

29 (35.4)

< 0.001

High TGL

102 (58.0)

52 (55.3)

50 (61)


High Blood Sugar

83 (47.2)

41 (43.6)

42 (51.2)


Data expressed as mean±SD, median (interquartile range), n (%). Percentages computed based on n for each sex.

Unpaired Stundent's t test, Mann-Whitney U or Chi2 among sexes, casewise.

eGFR: estimated glomerular filtration rates; BMI: Body Mass Index; WC: waist circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure; BUN: ureic nitrogen; TC: total cholesterol; LDLc: low-density lipoprotein cholesterol; HDLc: high-density lipoprotein cholesterol; TGL: tryglicerides; HBP: high blood pressure; SM: metabolic syndrome.

As compared to the group with normal eGFR, patients with mildly reduced eGFR (60 and 89 mL/min/1.73m2) showed significantly higher blood sugar levels (p= 0.012) and higher HbA1C (p= 0.014), and lower HDLc (p= 0.088); similar findings were observed when eGFR was < 90 mL/min/1.73m 2. On the other hand, total cholesterol levels (p=0.006) and LDLc (p= 0.002) in patients with hyperfiltration were significantly lower with respect to those found in individuals with normal eGFR.

Figure 1 compares eGFR with and without metabolic syndrome, showing eGFR was significantly lower in patients with metabolic syndrome (p=0.024 and p=0.013, respectively). The prevalence of metabolic syndrome was significantly higher in patients with chronic kidney disease, mildly reduced GFR, hyperfiltration or proteinuria (Fig. 2 and 3).




On the other hand, the number of urine protein crosses had a significant association with high blood pressure (p<0.005), high blood sugar (p<0.001), low HDLc (p<0.004) and higher numbers of metabolic syndrome components (p<0.007).

Logistic regression analysis revealed that both general and mild reductions in eGFR were significantly associated with high glycemia, low HDLc and metabolic syndrome, with and without adjusting for sex, age, and BMI. This association remained significant after adjusting for sex, age, BMI and HbA1C, the risk of general reductions in eGFR and mild reductions in eGFR (Table 2). When adjusting for sex, age and BMI, the risk of reduced eGFR rose significantly in association with the number of metabolic syndrome components. Thus, the odds ratio for eGFR < 90 mL/min/1.73m2 was of 1.51 (p=0.013) and for eGFR between 60-89 mL/min/1.73m 2 it was 1.50 (p=0.016), for each additional component.

Table N°2: adjusted and unadjusted odd ratio for reduced eGFR, metabolic syndrome, and each of its components.


eGFR <90 mL/min/1.73 m2

eGFR 60-89 mL/min/1.73 m2

Unadjusted OR

(95% CI)

OR adjusted for sex, age and BMI (95% CI)

OR adjusted for multiple variablesa

(95% CI)

Unadjusted OR (95% CI)

OR adjusted for sex, age and BMI (95% CI)

OR adjusted for muliple variables a (95% CI)

Abdominal obesity







High Blood Pressure







High Blood Tryglicerides







High Blood Sugar







Low HDLc

(1.13-4.08) *






Metabolic syndrome




(1.44-5.64) **

(1.60-7.12) **


* p<0.05; ** p<0.01, aplicando análisis regresión logística.

a adjusted for sex, age, BMI and glycosylated hemoglobin HbA1C.

OR: odds ratio; BMI: Body Mass Index; CI: confidence intervals.

Identical findings were made when assessing the associations between metabolic syndrome and the risk of proteinuria (one or more crosses in urine), with and without adjusting for sex, age and DMI (Table 3). After adjusting for sex, age, BMI and HbA1C, the risk of urine protein was still associated with metabolic syndrome. Notably, the association between proteinuria and high blood pressure became significant. The risk of proteinuria was associated with the number of metabolic syndrome components when adjusting for sex, age and BMI (OR: 1.70 for each additional component, p=0.004).

Hyperfiltration risks were not significantly associated with metabolic syndrome, its individual components, or the number of components affecting a patient.

Table N°3: Odds ratios with and without adjusting for proteinuria, metabolic syndrome, and each of its components.


Proteinuria (at least one cross in urine)

Unadjusted OR

(95% CI)

OR adjusted for sex, age and BMI

(95% CI)

OR adjusted for multiple variablesa

(95% CI)

Abdominal obesity




High Blood Pressure



(1.01-5.34) *

High Blood Tryglicerides




High Blood Sugar




Low HDLc




Metabolic syndrome

(1.04-3.86) *

(1.41-6.61) **

(1.02-5.78) *

* p<0.05; ** p<0.01, applying logistic regression analysis.

a Ajusted for sex, age, BMI and glycosylated hemoglobin HbA1C.

OR: odds ratio; BMI: Body Mass Index; CI: confidence intervals.


GFR is an indicator of glomerular filtration rate, which is the main indicator driving kidney disease diagnosis and treatment, and prognosis of kidney function. Interest has focused on severe reductions in glomerular filtration rates (< 60 mL/min/m 2). However, mild reductions or increases are worth considering as well, since they are commonly found in individuals at risk of developing chronic kidney disease.

In this study, chronic kidney disease (TFGe < 60 mL/min/m2) and proteinuria were significantly associated to female sex, in agreement with the results of a systematic review and meta-analysis21. So far there are no concrete explanations for these findings, and it has been suggested that thy might be due to biases in sampling, or that equations estimating eGFR could work better for detecting severe reductions in women, as proposed by Hill et al.21.

Glomerular filtration rates estimated using the CKD-EPI equation were significantly lower in patients with metabolic syndrome, and the frequency of metabolic syndrome was higher in patients with eGFR < 60 mL/min/1.73m2, confirming both the findings Chen et al.6 obtained when analysing a sample of urban patients in China, and the conclusions of a meta-analysis in 22. However, our main interest were early changes in glomerular filtration rates. We found the number of patients with metabolic syndrome was higher among patients who presented with small reductions in eGFR (60-89 mL/min/m2) and hyperfiltration, and among patients with at least one cross on reactive strips measuring urine protein.

Accordingly, in our sample the risk of hyperfiltration in general (eGFR < 90 mL/min/1.73m2) and mild reductions in eGFR specifically (between 60 and 89 mL/min/1.73m2) were significant in patients who presented with metabolic syndrome, and it multiplied up to three times when adjusting for age, sex, BMI; and HbA1C. For each additional component of metabolic syndrome found, the risk of reductions in eGFR and proteinuria increased between 1.5 and 1.7, respectively. These results are consistent with those obtained by researchers studying Chinese 8, Japanese10 and Korean23,24 patients. Moreover, they highlight the importance of the detection, prevention and eradication of metabolic syndrome in primary healtchare as a means of preventing the loss of glomerular function, given decreases in glomerular filtration rates predict the development of chronic kidney disease 12.

The impact of metabolic syndrome on the kidneys is still subject to controversy, and is likely multifactorial. Prasad25 proposes several distinct but interdependent mechanisms which operate simultaneously and contribute to kidney damage. Mytochondrial dysfunction, insulin resistence, oxidative stress, inflammatory processes and the expansion of fat tissue are recognized as the main mechanisms by which metabolic syndrome direectly affects kidney health. Specifically, insuline resistance causes endothelial dysfunction and injures podocytes, while oxidative stress and the expansion of fatty tissues induce the restructuring of kidney microvessels and tubulointerstitital fibrosis, which are mediated by endothelial dysfunction and the activation of renin-angiotensin-aldosterone and prinflammatory and prothrombotic adipokines, which in turn cause hypertension and microalbuminuria 26.

Increases in blood sugar above 100 mg/dL and reductions in HDLc were associated with a risk of proteinuria and low estimated glomerular filtration rates , with and without adjusting for sex, age and BMI (although this association disappeared when adjusting for HbA1C). Moreover, patients experiencing mild reductions in estimated glomerular filtration rates has significantly higher values of blood sugar and HbA1C. Its association with altered glycemia has been observed consistently in several works 6,8,25, but its relation to HDLc is not always attested. This last observation is biologically plausible since metabolic syndrome and type 2 diabetes share common physiopathological mechanisms (hyperfiltration, insulin resistance, oxidative stress, and inflammation), given which the initial stages of metabolic syndrome resemble early stages of diabetic nephropathy 26, although further studies on this matter are required.

Together with diabetes, arterial hypertension constitutes one of the main causes of kidney disease 1. In any case, a recent meta-analysis found that chronic kidney disease risks increased by 10% for every 10 mmHg increase in any of the two types of blood pressure. 27. In this work, proteinuria was significantly associated with high arterial pressure, and was the only component of metabolic syndrome that was significant after adjusting by sex, age, body-mass index, and HbA1 C, similar to the results obtained by Chen et al.6. This implies that, independently from glycemic control, mild arterial pressure elevation above 130/85 mmHg was enough to increase the risk of proteinuria almost threefold, being proteinuria an early sign of chronic kidney disease, which confirms it is necessary to attend to and follow up patients with altered arterial pressure.

Glomerular hyperfiltration or increases in glomerular filtration rates are early and reversible stages of kidney damage in hypertense and diabetic patients, and are considered a biomarker for the development of kidney disease, which can be detected before increases in microalbuminuria and reductions in gromerular filtration rates 10. Observing significantly lower total cholesterol and LDLc levels in patients with hyperfiltration as compared to patients with normal glomerular filtration rates was an unexpected result. However, Altay et al.28 have reported the same finding, and suggest autoimmune activation could be present in patients with glomerular hyperfiltration, which currently Onat et al.29 consider is a disruptor of the linear association between lipoprotein and chronic diseases like diabetes, within a state of inflammation and oxidative stress.

The expansion of fat tissue is probably the main factor causing glomerular hyperfiltration, which previous works associate with metabolic syndrome, by mechanisms that produce early damage and remodeling of glomerular and tubulointerstitial tissue, changing kidney hemodynamics 30. In our case, the frequency of metabolic syndrome was associated to metabolic syndrome, but hyperfiltration risk did not increase with metabolic syndrome or any of its components, which can be attributed to the low incidence of hyperfiltration in our sample, given which additional studies are required our population.

Here we address the limitations of our study: Glomerular filtration rates were not measured directly, and proteinuria was not checked using a second sample. Moreover, since this was a transversal study, it is not possible to establish causal relations between metabolic syndrome and glomerular filtration rate alterations. Thirdly, our sample was constituted by patients who sought specialized attention for patients with high cardiometabolic risk, given which our results cannot be generalized to the whole population.

In conclusion, we showed that the risk of small reductions in glmerular filtration rates (60-89 mL/min/m2) and the prescence of protein in urine was significantly higher in a group of adult patients of both sexes when there was metabolic syndrome, altered blood sugar or low HDLc, after adjusting for sex, age and body-mass index. After adjusting for blood sugar control, metabolic syndrome increased the risk of hypofiltration (estimated glomerular filtration rates < 90 mL/min/m2), mild reductions in estimated filtration rates (between 60 and 89 mL/min/m 2) and proteinuria, while high arterial pressure was associated to porteinuria. Hyperfiltration was not associated to metabolic syndrome. Longitudinal studies over a wider population are required to verify these findings.

Limitations of liability : this work is the exclusive responsibility of its authors.

Conflict of interest: the authors declare there are no conflicts of interest.

Funding: This research was conducted without funding from public, commercial or non-profit organizations.

Derivative work notice:

Obra derivada: Traducción del artículo "Función glomerular y síndrome metabólico en adultos venezolanos con factores de riesgo cardiometabólico atendidos en un centro de atención primaria", escrito por Leal et al, publicada en Rev Fac Cien Med Univ Nac Cordoba. 2023; 76 (3), realizada por la Revista de la Facultad de Ciencias Médicas de Córdoba

This derivative work is a translation of the article "Función glomerular y síndrome metabólico en adultos venezolanos con factores de riesgo cardiometabólico atendidos en un centro de atención primaria", authored by Leal et al, published in Rev Fac Cien Med Univ Nac Cordoba. 2023; 76 (3), produced by Revista de la Facultad de Ciencias Médicas de Córdoba


1. Drawz P, Rahman M. Chronic kidney disease. Ann Intern Med 2015:162(11):ITC1-16.

2. Jha V, García-García G, Iseki K, Li Z, Naicker S, Plattner B et al. Chronic kidney disease: global dimension and perspectives. Lancet 2013; 382(9888):260-272.

3. Ministerio del Poder Popular para la Salud. Anuario de Mortalidad 2013. Caracas, Venezuela: Ministerio del Poder Popular para la Salud; 2017.

4. Ardhanari S, Alpert MA, Aggarwal K. Cardiovascular disease in chronic kidney disease: risk factors, pathogenesis, and prevention. Adv Perit Dial 2014;30:40-53.

5. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120(16):1640-5.

6. Chen J, Kong X, Jia X, Li W, Wang Z, Cui M et al. Association between metabolic syndrome and chronic kidney disease in a Chinese urban population. Clin Chim Acta 2017;470:103-8.

7. Onat A, Hergenç G, Uyarel H, Ozhan H, Esen AM, Karabulut A et al. Association between mild renal dysfunction and insulin resistance or metabolic syndrome in a random nondiabetic population sample. Kidney Blood Press Res 2007;30(2):88-96.

8. Hu W, Wu XJ, Ni YJ, Hao HR, Yu WN, Zhou HW. Metabolic syndrome is independently associated with a mildly reduced estimated glomerular filtration rate: a cross-sectional study. BMC Nephrol 2017;18(1):192.

9. Monami M, Pala L, Bardini G, Francesconi P, Cresci B, Marchionni N et al. Glomerular hyperfiltration and metabolic syndrome: results from the FIrenze-BAgno A Ripoli (FIBAR) Study. Acta Diabetol 2009;46(3):191-6.

10. Okada R, Yasuda Y, Tsushita K, Wakai K, Hamajima N, Matsuo S. The number of metabolic syndrome components is a good risk indicator for both early- and late-stage kidney damage. Nutr Metab Cardiovasc Dis 2014; 24(3):277-285.

11. Henry RM, Kostense PJ, Bos G, Dekker JM, Nijpels G, Heine RJ et al. Mild renal insufficiency is associated with increased cardiovascular mortality: The Hoorn Study. Kidney Int 2002;62(4):1402-7.

12. Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D. Predictors of new-onset kidney disease in a community-based population. JAMA 2004;291(7):844-50.

13. Park M, Yoon E, Lim YH, Kim H, Choi J, Yoon HJ. Renal hyperfiltration as a novel marker of all-cause mortality. J Am Soc Nephrol 2015;26(6):1426-33.

14. Kwon Y, Han K, Kim YH, Park S, Kim DH, Roh YK et al. Dipstick proteinuria predicts all-cause mortality in general population: A study of 17 million Korean adults. PLoS One 2018;13(6):e0199913.

15. O'Brien E, Asmar R, Beilin L, Imai Y, Mancia G, Mengden T et al. Practice guidelines of the European Society of Hypertension for clinic, ambulatory and self blood pressure measurement. J Hypertens 2005;23(4):697-701.

16. World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 1995;854:1-452.

17. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150(9):604-612.

18. National Kidney Foundation. K/KDOQI Clinical practice guidelines for chronic kidney disease evaluation, classification and stratification. Am J Kidney Dis 2002;39(2 Suppl 1):S1-266.

19. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA 2003;289(19):2560-2571.

20. American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care 2015; 38 Suppl:S8-S16.

21. Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS et al. Global Prevalence of Chronic Kidney Disease–A Systematic Review and Meta-Analysis. PLoS One 2016;11(7):e0158765.

22. Thomas G, Sehgal AR, Kashyap SR, Srinivas TR, Kirwan JP, Navaneethan SD. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol 2011;6(10):2364-2373.

23. Kim JK, Ju YS, Moon SJ, Song YR, Kim HJ, Kim SG. High pulse pressure and metabolic syndrome are associated with proteinuria in young adult women. BMC Nephrol 2013;14:45.

24. Hong N, Oh J, Lee YH, Youn JC, Park S, Lee SH et al. Comparison of association of glomerular filtration rate with metabolic syndrome in a community-based population using the CKD-EPI and MDRD study equations. Clin Chim Acta 2014;429:157-162.

25. Prasad GV. Metabolic syndrome and chronic kidney disease: Current status and future directions. World J Nephrol 2014;3(4):210-19.

26. Zhang X, Lerman LO. The metabolic syndrome and chronic kidney disease. Transl Res 2017;183:14-25.

27. Garofalo C, Borrelli S, Pacilio M, Minutolo R, Chiodini P, De Nicola L et al. Hypertension and Prehypertension and Prediction of Development of Decreased Estimated GFR in the General Population: A Meta-analysis of Cohort Studies. Am J Kidney Dis 2016;67(1):89-97.

28. Altay S, Onat A, Özpamuk-Karadeniz F, Karadeniz Y, Kemaloğlu-Öz T, Can G. Renal "hyperfiltrators" are at elevated risk of death and chronic diseases. BMC Nephrol 2014;15:160.

29. Onat A, Kaya A, Ademoglu E. Modified risk associations of lipoproteins and apolipoproteins by chronic low-grade inflammation. Expert Rev Cardiovasc Ther 2018;16(1):39-48.

30. Gluba A, Mikhailidis DP, Lip GY, Hannam S, Rysz J, Banach M. Metabolic syndrome and renal disease. Int J Cardiol 2013;64(2):141-150.

Recibido: 2019-03-13 Aceptado: 2019-06-06