Heterogeneity in the Association Between Prediabetes Categories and Development of Impaired Renal Function in A 5-Year Follow-Up&nbsp;

doi:10.21203/rs.3.rs-110114/v1

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Heterogeneity in the Association Between Prediabetes Categories and Development of Impaired Renal Function in A 5-Year Follow-Up

https://doi.org/10.21203/rs.3.rs-110114/v1

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Background: Impaired renal function (IRF) in patients with diabetes impose a significant health burden. This study evaluates the association of three diagnostic categories of with a reduction on glomerular filtration rate

Methods: We used data from PREDAPS prospective study a cohort of 1072 subjects with prediabetes and another cohort of 772 subjects without prediabetes were follow-up from 2012 to 2017. Prediabetes was defined according to American Association of Diabetes (ADA) criteria. IRF was defined as having a glomerular filtration rate < 60 mL/min/1.73 m2. Incidence rate of IRF according to prediabetes and diagnostic categories of prediabetes as well as Kaplan Meir figures were calculated. Hazard ratios (HR) for the association of the prediabetes diagnostic categories of prediabetes with IRF, adjusting for potential confounders, were estimated by Cox regression models.

Results: IRF Incidence rate per 100 person-years in subjects without prediabetes was 1.72 (95% confidence interval [CI]: 1.34-2.21) and 1.79 (95%CI: 1.45-2.20) for those with prediabetes stage, log rank test p= 0.84. Prediabetes was associated with an adjusted HR of IRF onset of 0.76 (95% CI: 0. 54-1.07). Corresponding HR for diagnostic categories were 0.68 (95%CI:0.40-1.15) for those with both altered parameters, 0.68 (95%CI: 00.40-1.15) for those with only impaired HbA1c and 1.12 (95%CI: 0.68-1.85) for those with only impaired FPG.

Conclusions: The present study reflects an overall trend towards a slightly decreased risk of IRF onset associated to prediabetes except for individuals with only isolated impaired FPG. Further studies are warranted to fully assess the renal progression of each group.

Urology & Nephrology

IRF

diabetes

patients

hyperfiltration

Approximately 422 million people globally suffer from diabetes globally and 1.6 million deaths are directly attributed to this each year. [1-2] Type 2 diabetes (T2D) is the most common, resulting as a result of increased insulin resistance.. Diabetes is among the leading causes of chronic kidney failure around the world. [2] Impaired renal function (IRF) in patients with diabetes impose a significant health burden.[3] Deterioration of the renal function in combination with diabetes can lead to poorer health prognosis. [4]

It has been reported that up to 40% of patients in early stage of T2D demonstrate some degree of microvascular complication.[5] In addition, a high proportion of patients with diabetes are found to have non-diabetic renal disease (NDRD), being nephroangiosclerosis (NAS) the most frequent cause [6-7] Metabolic changes associated with diabetes lead to glomerular hypertrophy, glomerulosclerosis, and tubulointerstitial inflammation and fibrosis. [5] In addition, according to various studies, one-third of adults with newly diagnosed diabetes mellitus already have kidney damage, suggesting that IRF may occur in pre-diabetic state. [8] The effect of hyperglycemia on the occurrence of IRF may start before glucose levels reaches diabetic ranges. Other diabetes-related microvascular complications, such as retinopathy and neuropathy have been described in some previous studies, in subjects with prediabetes. [9-10]

World Health Organization (WHO) has emphasized the early detection of IRF as an important component of its strategies for prevention of noncommunicable diseases. [10] However, the long-term influences of prediabetes on kidney function remains unknown. In order to fill major gaps in the role of prediabetes on IRF onset, the aim of this study was to evaluate the association between prediabetes and three diagnostic categories of prediabetes and incidence rate of IRF using a prospective cohort of individuals with prediabetes followed up by primary care physicians from Spain.

Study design

The Cohort Study in Primary Health Care on the Evolution of Patients with Prediabetes (PREDAPS), is a prospective study conducted by 125 Primary Care physicians at their practices from different provinces of Spain. The details of the cohort have been described elsewhere.[11-12] The age range of patients was between 30 and 74 years. The study period started in 2012 and continued up to the fifth annual follow-up visit in 2017. All individuals with the following criteria at baseline were excluded: diagnosis of diabetes, terminal disease, pregnancy, surgery, hospital admissions in the previous 3 months at study entry or any hematologic disease which could alter glycated hemoglobin A1c (HbA1c) values. The estimate glomerular filtration rate (eGFR) at baseline was calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) [9]. A total of 92 subjects did not have measures eGFR at baseline and an additional 66 patients had values below 60 mL/min/1.73 m2 min which is already considered as a reduction; therefore, they were excluded due to this pre-existing condition. Final population included a total of 1844 subjects.

Study population was subdivided into two mutually exclusive cohorts according to glycemic parameters following American Diabetes Association (ADA) criteria: cohort of subjects with prediabetes (n=1,072) and cohort of subjects without prediabetes (N=772). Prediabetic cohort was subdivided into three mutually exclusive diagnostic categories based on impaired glycemic parameters. First category, included all subjects with only impaired fasting plasma glucose (FPG range: 100-125 mg/dL [5.6-6.9 mmol/L]),, second category included all subjects with isolated impaired HbA1c (HbA1c range: 5.7-6.4% [39-47 mmol/mol]) and third category included subjects with both impaired glycemic parameters (FPG and HbA1c) [13]

Study period started on 2012 up to the fifth follow-up visit. Once meeting the eligibility criteria, individuals were followed up from baseline until the occurrence of one of the following end points: i) IRF ii) death, iii) loss of follow up or iv) end of study period (2017), whichever came first. IRF occurrence was measured at each annual visit and considered when a subject presented an eGFR < 60 mL/min/1.73 m2 during the follow up. IRF patients were immediately censored within the follow up.

Subjects gave their written informed consent for participation. The study was classified by the Spanish Agency of Medicines and Medical Devices as a Non-Interventional (Observational) Post-Authorization Study, and the study protocol was approved by the Parc de Salut Mar Clinical Research Ethics Committee in Barcelona (2011-4274-I).

Assessment of covariables

Data were collected at the first visit (baseline period). Information on biographical data, family history, comorbidities, demographical data (i.e. including social support and socio-economic position), lifestyle factors, and drug use were obtained from medical records of study subjects as well as a personal interview conducted by the physicians.

Comorbidities were categorized as follows. Hypertension (HTN) classified as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, being treated by antihypertensive drugs or previous diagnosis of HTN. Hypercholesterolemia, as serum total cholesterol ≥250 mg/dL, HDL-C as <40 mg/dL in men or <50 mg/dL in women, and hypertriglyceridemia (HTG) as serum triglycerides ≥200 mg/dL.

Lifestyle factors were categorized as follows: Body mass index (BMI) between 25.0 and 29.9 kg/m²classified as overweight, general obesity defined as BMI ≥30 kg/m²; abdominal obesity defined as waist circumference ≥102 cm in men and ≥88 cm in women. Smoking categories into smokers, ex-smokers and non-smokers; Alcohol consumption as: daily drinkers, occasional drinkers, and non-drinkers (never or former) which included ex-drinkers and teetotalers. Physical activity was classified according to World Health Organization (WHO) recommendations. Subjects followed the recommendations if they practiced more than 150 minutes per week of moderate aerobic physical activity, more than 75 minutes each week of vigorous aerobic physical activity or an equivalent combination.[14] For the adherence to Mediterranean diet (MedDiet), it was used as reference the definition followed in ATTICA study and their designed Panagiotakos score.[15] For each twenty types of studied nutrients, subjects responded the frequency of consumption: every day, more than three times a week, two times each week, once a week, less than once a week, never or rarely. Zero as a score in each meal was considered if the subject was having a less healthy diet and 4 was considered if the subject was having a very healthy diet.

Statistical analysis

First of all, a descriptive analysis was conducted showing the distribution of the baseline characteristics among those who were classified in prediabetic cohort and those in the cohort without prediabetes. Continuous and count variables were described using mean (± standard deviation [SD]), median (quartiles) and 95% confidence intervals (95%CI). Incidence rate of IRF per 100 person-years together with 95%CI were calculated in each cohort. Incidence rate of IRF according to prediabetes categories was also calculated. Kaplan Meier survival functions with log rank test were performed to compare the survival distributions across each group. Cox proportional hazards analyses were used to estimate the hazard ratios (HR) with 95%CI for the association of covariables with incidence rate of IRF and for the association of prediabetes and diagnostic categories of prediabetes with reduction of incidence rate of IRF. Results of the association of prediabetes and diagnostic categories of prediabetes with incidence rate of IRF onset were shown for four levels of adjustment: model 1 (adjusted by age and sex), model 2 (model 1 plus adjusted by lifestyle variables such as smoking status, regular physical activity, high-risk alcohol consumption, adherence to MedDiet score, model 3 (model 2 plus adjusted by metabolic risk factors such as waist circumference, BMI, hypertension (HTN), total cholesterol, low HDL-cholesterol, triglycerides), and model 4 (model 3 plus adjusted by use of angiotensin converting enzyme inhibitors [ACEIs] or angiotensin II receptor blockers [ARBs]).). Statistical analyses were performed using the STATA package version 12.0 (StataCorp LP, College Station, TX, USA).

Baseline characteristics

A total of 1,844 patients (95% Spanish origin) were included in the present study, from whom 1,072 (58.1%) had prediabetes according to the ADA guidelines the mean age of prediabetic and normoglycemic groups were 59.1 (SD 9.3) and 56.6 (SD 10.3) years, respectively. At baseline, lifestyle factors were similarly distributed among patients with prediabetes compared with normoglycemia group. In terms of comorbidities, more than half of patients with prediabetes presented metabolic syndrome compared with 12.4 % in the normoglycemic group. Likewise, the prevalence of HTN was higher in the prediabetic group. In particular, 36.9% of patients with prediabetes had treatment with ACEIs or ARBs drugs versus 23.6% in the normoglycemia group. For all metabolic parameters measured, prediabetic group presented a higher mean (p value <0.01) with the exception of total cholesterol which the distribution was almost the same (210 mg/dL). Finally, the mean value of eGFR (mL/min/1.73 m2) was very similar across groups: 89.1 (SD:13.7) for prediabetic cohort and 90.4 (SD:13.4) for normoglycemic cohort. (Table 1)

Incidence rate of IRF overall and by prediabetes categories

A total of 88 incident cases of IRF occurred in the prediabetic group and 61 cases occurred in the normoglycemic group. Incidence rates of IRF among the two study groups, overall and by prediabetes categories, are shown in Table 2. The overall incidence rate of IRF per 100 person-years was 1.72 (95%CI: 1.34-2.21) and 1.79 (95%CI: 1.45-2.20), log rank test p= 0.84. Focusing on the prediabetic group, the incidence of IRF was lower in the HbA1c 5.7- 6.4 % group (IR: 1.40 [95%CI: 0.89-2.19]) and highest among those with isolated FPG 100-125 mg/dL (IR: 2.06 [95%CI: 1.36-3.13]) log rank test p= 0.74. Figure 1 shows the Kaplan Meier survival function of IRF by type of cohort and Figure 2 by prediabetes categories.

Covariables and incidence rate of IRF

Table 3 shows the risk factors associated to IRF onset sex- and age-adjusted. There was a trend towards and increase risk of IRF according to age, for example those aged 50 to 64 years had a HR of 5,11 (95%CI: 1.85-14.12) and 16.48 (95% CI: 6.06-44.85) for those aged ≥65 years. Adherence to MedDiet showed a protective effect against reduction of eGFR (HR: 0.75 (95%CI: 0.54-1.04). On the contrary, metabolic conditions such as waist circumference ≥102 cm in men and ≥88 cm in women or having a BMI ≥30 were associated with an increased risk of reduction of eGFR (HR: 1.39 [95%CI: 0.54-1.04] and 1.21 [95%CI: 0.87-1.68], respectively). History of HTN showed a HR of 2.08 (95%CI: 1.38-3.12) and 1.82 (95% CI: 1.31-2.52) for use of ACEIs/ARBs but there was not association with metabolic syndrome (1.14 [95%CI: 0.82-1.59]). Having levels of HDL-cholesterol (mg/dL) as <40 mg/dL in men or <50 mg/dL was associated with a HR of IRF of 1.28(95%CI: 0.86-1.91).

Association of prediabetes and diagnostic categories of prediabetes with incidence rate of IRF

Results are shown in Table 4. Using the cohort of subjects without prediabetes as reference, prediabetes was associated with a HR of IRF onset of 0.89 (95%CI: 0.64-1.24) when adjusting by age and sex. This estimate remained the same when adding lifestyle variables to the model and HR decreased to 0.76 (95%CI: 0.54-1.07) when adding metabolic conditions together with lifestyle factors. Of note, the estimate remained constant when adding on top of this model use of ACEIs or ARBs (Table 4). When evaluating the risk of IRF onset according to prediabetes diagnostic categories, a trend towards a decreased risk of IRF onset was observed in subjects with both parameters altered (FPG and HbA1c) and those with only impaired HbA1c levels, corresponding HR estimates were 0.68 (95%CI :0.40-1.15) and 0.68 (95%CI: 0.40-1.15), respectively. However, subjects with only impaired FPG did not show any association (HR: 1.12 [95%CI: 0.68-1.85]).

Findings of this prospective cohort study reflect an overall trend towards a slightly decreased risk of IRF onset associated to prediabetes with an adjusted HR of 0.76. This finding is restricted to subjects who only had impaired HbA1c and those who had both parameters impaired: both groups represent 80% of the subjects with prediabetes and their adjusted HR was 0.76. Instead, subjects with only impaired FPG levels had a slightly increased risk (adjusted HR=1.12).

A recent meta-analysis, including a total of eight cohort studies with subjects with impaired FPG as prediabetes criteria, has also reported a modest increased risk of IRF associated to impaired FPG [16]. It is known that hyperglycemia increases the production of reactive oxygen species, which lead to the accumulation of advanced glycation end products. This, in turn, activate intracellular signaling pathways such as protein kinase C and intensify the effects of the renin-angiotensin system.[5] This effect may lead to early onset of glomerular hyperfiltration and subsequently a decreased of IRF onset. In addition, eGFR has been reported to decrease faster in patients with hyperfiltration which might lead to kidney damage occurrence. [17-18] Although there is still controversy towards if hyperfiltration occurs in the early stages of hyperglycemia, several studies have found significant associations between hyperfiltration and prediabetes.[19,20] Specifically, it has been suggested how the prevalence of hyperfiltration increases with worsening stages of prediabetes. [19].

The reduced risk of IRF was concentrated among subjects who only had impaired HbA1c and those who had both parameters, while subjects with isolated impaired FPG showed a slightly increased risk of IRF, suggesting that these subjects might still preserve the renal function, and this is not via hyperfiltration. These results are consistent with a prior study using the same study population where it was found how individuals with impaired of both FPG and HbA1c had an OR of hyperfiltration of 1.69 (95% CI: 1.05–2.74) while there was no association among individuals with solely impaired FPG levels [22].

World Health Organization (WHO) has emphasized the early detection of IRF as an important component of its strategies for prevention of noncommunicable diseases, [10] as this has proven to improve outcomes for both individual and national healthcare economy. Since hyperfiltration is thought to be an early and proxy to reversible stage of kidney damage [21] monitoring and identifying high risk prediabetic patients might result as an effective and cost-efficient preventive strategy. For example, both FPG and HbA1C levels can serve as chemical marker to identify early deterioration of IRF and avoid nephropathy. Another advantage of intensive blood glucose control in the prediabetic state can be seen in the long-term protective effect known as metabolic memory.[13] Thus, early intensive glycemic control could prevent irreversible damage that has been associated with hyperglycemia through closer monitoring of patients.[22] A 24% reduction in microvascular complications, including IRF, compared with tight glycemic control has been found in another study that followed up subjects with T2D for up to ten years. [23] Intensive glycemic control resulted in a 33% reduction in the risk of microproteinuria, proteinuria. Also a significant reduction in the proportion of patients with a doubling of the blood creatinine level (0.9% versus 3.5%) relative to the conventional therapy group was oberved. [23]

Findings of the present study show that baseline risk factors such as (e.g. age, sex, ethnicity, and family history) and other factors (e.g., acute kidney injury, HTN, dietary factors, and obesity) can lead to diabetes kidney disease. [16] Hypertension is the leading cause of Impaired renal function. [19, 21,23] Observational studies have indicated that blood pressure increases with age, adding to the long-term population burden of IRF. According he Kidney Early Evaluation Program (KEEP) and NHANES project, there is an increase in the prevalence and severity of IRF with increasing of age, as expected. [24] Also, abdominal obesity, measured as waist circumference, show an increased risk of IRF, although did not reach statistical significance. Regarding nutritional therapy in IRF patients is still under debate. Although nutritional therapy was implemented to reduce amount of high biological value proteins, currently the interest is also focus on the possibility of delaying IRF progression. [17,25] The current study found a protective effect against IRF onset associated with high adherence to the MedDiet, characterized by high consumption of extra-virgin olive oil, nuts, red wine, vegetables and polyphenol-rich elements (HR: 0.75 [95%CI: 0.54-1.04]).

This study reflects that it is possible to carry out a prospective study, with data obtained at the national level by primary care physicians during clinical practice. However, analytical determinations were made in different laboratories, which could have led to some misclassification. Given that each subject was assigned to the same laboratory during follow-up, this limitation should be non-differential in relation to the result, since an association between the methods used by specific laboratories and the development of IRF is unlikely.

This study used the IRF-EPI creatinine-based equation. This equation is more accurate and has less bias than the commonly used Diet Modification in Kidney Disease (MDRD) equation, especially at higher GFR levels. [9,26] In addition, GFR was estimated rather than using the gold standard of insulin clearance for this measure. Insulin clearance is more accurate than eGFR, but it is not cost-effective and it is an invasive method that is not used in clinical practice in Primary Care on a daily basis. However, it should be noted that eGFR was calculated from a single serum creatinine measurement. However, this classification is unlikely to be differential with respect to prediabetes status.

Finally, researchers were unable to determine a time-dependent variable. However, the vast majority of the factors considered in the present study are chronic conditions or long-term lifestyle factors not susceptible to a fast variation within the follow-up during the study period.

Prediabetes is a temporary stage from normal glucose tolerance to diabetes which can be associated with long term macro or micro vascular complications. The present study did not show an increased risk of IRF onset associated to prediabetes, with the exception of those with isolated impaired FPG. Further study is warranted to test the effect of these risk factors on the various prediabetic state in the multinational large cohort on the reduction of IRF onset.

ACEIs - Angiotensin converting enzyme inhibitors

ARBs - Angiotensin II receptor blockers

ADA - American Diabetes Association

BMI - Body mass index

IRF - Impaired renal function

Cr - Creatinine

DBP - Diastolic blood pressure

FPG - Fasting plasma glucose

GFR - Glomerular filtration rate

HbA1c - Glycated hemoglobin A1c

HDL - High-density lipoprotein

MedDiet- Mediterranean Diet

NS - Not significant

OGTT - Oral glucose tolerance test

SD - Standard deviation

SBP - Systolic blood pressure

T2DM - Type 2 diabetes

WC - Waist circumference

Ethics approval and consent to participate: The study was classified by the Spanish Drug and Health Product Agency as a Non-Interventional (Observational) Post-Authorization Study, and the protocol was approved by the Parc de Salut Mar Clinical Research Ethics Committee in Barcelona. Informed consent was obtained from all participants and/or their legal guardians

Method and Declaration section: Authors confirm that all methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication: Non-applied

Availability of data and materials: The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Competing interests None declared

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

Authors' contributions: MM, LCS and ER analyzed and interpreted the data. LCS, ER and JFN wrote the outline and edited the manuscript. AR, AG, RV, JD-E , MM-C and CG-G- reviewed/edited the manuscript and contributed to discussion. As reviewed and edited the manuscript. All authors read and approved the final manuscript

Guarantor’s: Dr. Regidor is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest: none declared.

Acknowledgements:

The other members of the PREDAPS Study Group are:

Margarita Alonso (CS De la Eria, Asturias), Beatriz Álvarez (CS Andrés Mellado, Madrid), Fernando Álvarez (CS La Calzada 2 , Asturias), J Carlos Álvarez (CS Eras de Renueva, León), Mª del Mar Álvarez (CS Hereza, Madrid), J Joaquín Antón (CS Murcia-Centro, Murcia), Oriol Armengol (EAP Poblenou, Barcelona), Sara Artola (CS José Marvá, Madrid), Luis Ávila (Consultorio Almachar, Málaga), Carmen Babace (CS Rodríguez Paterna, La Rioja), Lourdes Barutell (CS Andrés Mellado, Madrid), Mª Jesús Bedoya (CS Hereza, Madrid), Belén Benito (EAP Raval Sud , Barcelona), Beatriz Bilbeny (EAP Raval Sud, Barcelona), Marti Birules (EAP Poblenou , Barcelona), Concepción Blanco (CS Sada, A Coruña), Mª Isabel Bobé (EAP La Mina, Barcelona), Carmen Boente (CS Porriño, Pontevedra), Antonia Borras (CS Canal Salat, Baleares), Remei Bosch (EAP Girona 2 , Girona), Mª Jesús Brito (CS La Matanza, Baleares), Pilar Buil (EAP Azpilagaña, Navarra), J José Cabré (EAP Reus-1 , Tarragona), Ainoha Cambra (CS Arrabal, Zaragoza), Francisco Carbonell (CS Mislata, Valencia), Francisco Carramiñana (CS San Roque, Badajoz), Lourdes Carrillo (CS La Victoria de Acentejo, Santa Cruz de Tenerife), Ana Casorrán (CS Fuente de San Luis, Valencia), Rafael Colas (CS Santoña, Cantabria), Blanca Cordero (CS Sta. María de Benquerencia, Toledo), Xavier Cos (EAP Sant Martí de Provençals, Barcelona), Gabriel Cuatrecasas (CAP de Sarrià, Barcelona), Cristina De Castro (CS Sta. María de Benquerencia, Toledo), Manuel De la Flor (CS Ntra. Sra. de Gracia , Sevilla), Carlos De la Sen (Consultorio San Gabriel, Alicante), Rosa Mar De Miguel (EAP Pubillas Casas, Barcelona), A María De Santiago (Unidad Docente de Atención Familiar y Comunitaria, Guadalajara), Mercedes Del Castillo (CS Andrés Mellado, Madrid), Mª Carmen Durán (CS Lavadores Vigo, Pontevedra), Patxi Ezkurra (CS Zumaia, Guipúzcoa), Paula Gabriel (EAP Badia del Vallès , Barcelona), Javier Gamarra (CS Medina del Campo Rural, Valladolid), Francisco García (CS Don Benito Este, Badajoz), Luis García-Giralda (CS Murcia Centro, Murcia), F Javier García-Soidán (CS Porriño, Pontevedra), Mª Teresa Gijón (CS Los Yébenes, Madrid), Albert Goday (Endocrinología Hospital del Mar, Barcelona), Ángel Gómez (CS Lasarte, Guipúzcoa), María del Carmen Gómez (CS Vélez-Málaga Norte, Málaga), J Carles González (Girona 3, Girona), María González (CS Alcantarilla Sangonera, Murcia), Esteban Granero (CS Vista Alegre Murcia , Murcia), Ángela Trinidad Gutiérrez (CS El Calero, Las Palmas), Félix Gutiérrez (CS Bombarda-Monsalud, Zaragoza), Luisa Gutiérrez (CS Beraun, Guipúzcoa), M Ángel Gutiérrez (CS Ávila Sur Oeste, Ávila), Ana M^a Hernández (CS El Calero, Las Palmas ), Mercedes Ibáñez (CS Vandel, Madrid), Rosario Iglesias (CS Lain Entralgo, Madrid), Dimas Igual (CS Manuel Encinas, Cáceres), Jaime Innenaraty (CS Hereza Leganes, Madrid), Jaime Iriarte (CS Aizarnazabal-Getaria, Guipúzcua), Ángeles Jurado (CS Salvador Caballero, Granada), Rafael Llanes (Villanueva de la Cañada, Madrid), Flora López (EAP Martorell , Barcelona), Regina López (EAP El Carmel, Barcelona), Riánsares López (CS Artilleros, Madrid), Ángela Lorenzo (CS Alcalá de Guadaira, Madrid), Carmen Losada (UGC Adoratrices, Huelva), Ramón Macía (CS Roces Montevil, Asturias), Fernando Malo (CS Ares, A Coruña), José Mancera (CS Ciudad Jardín, Málaga), Mª José Mansilla (CS Martín de Vargas , Madrid), Mª Teresa Marín (CS General Ricardos, Madrid), José Luis Martín (CS Salvador Caballero, Granada), F Javier Martínez (CS Federica Monseny, Madrid), Juan Martínez (CS Yecla, Murcia), Mª del Carmen Martínez (EAP Raval Sud, Barcelona), Rosario Martínez (CS Oñati, Guipúzcoa), Anna Massana (EAP Raval Sud), Mª Soledad Mayayo (CS Martín de Vargas , Madrid), J Javier Mediavilla (CS Burgos Rural, Burgos), Luis Mendo (CS Cadreita, Navarra), J Manuel Millaruelo (CS Torrero La Paz, Zaragoza), Alicia Monzón (CS Vecindario, Las Palmas ), Ana Moreno (CAP San Roque, Badajoz), Pedro Muñoz (Unidad Docente de Medicina Familiar y Comunitaria, Cantabria), Xavier Mundet (EAP Carmel, Barcelona), Teresa Mur ( CAP Terrasa Sud, Barcelona), Emma Navarro (CS Añaza, Santa Cruz de Tenerife), Jorge Navarro (CS Salvador Pau, Valencia), Pedro Nogales (CS Las Águilas, Madrid), J Carlos Obaya (CS Chopera, Madrid), Cristina Oria (CS Aizarnazabal-Guetaria, Guipúzcoa), Francisco Javier Ortega (CS Campos-Lampreana, Zamora), Francisca Paniagua (CS Ciudad Jardín, Málaga), José Luis Pardo (CS Orihuela I, Alicante), Francisco Carlos Pérez (CS Martín de Vargas, Madrid), Pedro P Pérez (CS Mallen, Sevilla), Neus Piulats (EAP Raval Sud , Barcelona), Raquel Plana (CS Ponteareas, Pontevedra), Nuria Porta (CAP Terrassa Sud , Barcelona), Santiago Poveda (CS Jumilla, Murcia), Luis Prieto (CS Cáceres-La Mejostilla, Cáceres), Ramón Pujol (EAP Tremp, Lleida), Sol Reixa (CS Arrabal, Zaragoza), Jazmín Ripoll (CS Fuente de San Luis, Valencia), Antonio Rodríguez (EAP Anglès, Girona), J José Rodríguez (CS Villaviciosa de Odón, Madrid), Mª Angeles Rollán (CS Los Yébenes, Madrid), Mª Teresa Rollán (CS Hereza, Madrid), Laura Romera (EAP Raval Nord, Barcelona), Pilar Roura (EAP Badia de Vallès, Barcelona), Jóse Félix Rubio (CS Lasarte, Guipúzcua), Irene Ruiz (EAP La Torrassa, Barcelona), Manuel Antonio Ruiz (CS Agost, Alicante), Isabel Sáenz (CS Espronceda, Madrid), Julio Sagredo (CS Los Rosales, Madrid), Alejandro Salanova (CS Fuente de San Luis, Valencia), L Gabriel Sánchez (CS Carballeda, Zamora), Manuel Sánchez (CS Vista Alegre Murcia, Murcia), Gloria Sanz (CS San José centro, Zaragoza), Mateu Seguí (UBS Es Castell, Baleares), Josefina Servent (EAP El Carmel, Barcelona), Dulce Suárez (CS El Calero, Las Palmas ), Eduard Tarragó (EAP Bellvitge, Barcelona), Jesús Torrecilla (CS Bombarda-Monsalud, Zaragoza), José Luis Torres (CS Rodríguez Paterna, La Rioja), Merè Villaró (CAP Terrassa sud, Barcelona), Carmen Yuste (EAP Badia del Vallès , Barcelona).

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Table 1. Demographic and clinical characteristics of participants at baseline.



Characteristics	Prediabetes	Normoglucose	p value
Characteristics	(n=1,072)	(n=772)	p value
Age (years), mean (SD)	59.1 (9.3)	56.6 (10.3)	<0.001
Male, n (%)	536 (50.0)	358 (46.4)	0.068
Smoking status, n(%)
Active smoker	182 (17.0)	170 (22.0)	0.003
Ex-smoker	409 (38.2)	244 (31.6)
Never smoker	481 (44.9)	358 (46.4)
Regular physical activity, n(%)	575 (53.7)	428 (55.4)	0.249
High-risk alcohol consumption, n(%)	140 (13.1)	83 (10.8)	0.078
Adherence Mediterranean diet score, n(%)	564 (52.6)	370 (47.9)	0.026
Daily consumption of fruit or vegetables, n(%)	919 (85.7)	653 (84.6)	0.269
Metabolic syndrome, n(%)	559 (52.1)	96 (12.4)	<0.001
Waist circumference (cm), mean (SD)	100.1 (12.3)	93.0 (11.8)	<0.001
BMI (kg/m²), mean (SD)	29.9 (4.8)	27.4 (4.4)	<0.001
Fasting plasma glucose (mg/dL), mean (SD)	105.2 (10.9)	87.0 (7.2)	<0.001
HbA1c (%), mean (SD)	5.8 (0.3)	5.3 (0.3)	<0.001
Hemoglobin (g/dL), n(%)
≥13.0	975 (91.0)	665 (86.1)	0.005
12.9-11.0	91 (8.5)	102 (13.2)
≤10.9	6 (0.6)	5 (0.6)
Hypertension, n(%)	708 (66.0)	359 (46.5)	<0.001
SBP (mmHg), mean (SD)	134.6 (16.0)	128.3 (15.3)	<0.001
DBP (mmHg), mean (SD)	81.2 (9.4)	79.0 (9.5)	<0.001
Total colesterol (mg/dL), mean (SD)	209.9 (37.4)	211.0 (37.5)	0.532
HDL-cholesterol (mg/dL), mean (SD)	54.4 (14.3)	58.5 (15.6)	<0.001
Non HDL-cholesterol (mg/dL), mean (SD)	129.6 (33.9)	129.8 (32.2)	0.899
Triglycerides (mg/dL), mean (SD)	132.3 (71.7)	114.4 (75.6)	<0.001
Use of ACEIs or ARBs, n(%)	396 (36.9)	182 (23.6)	<0.001
Creatinine(mg/dL), mean (SD)	0.8 (0.2)	0.8 (0.2)	0.760
eGFR (mL/min per 1.73 m²), mean (SD)	89.1 (13.7)	90.4 (13.4)	0.060
SBP= systolic blood pressure; DBP= diastolic blood pressure; ACEIs=angiotensin converting enzyme inhibitors; ARBs=angiotensin receptor blockers; eGFR=estimated glomerular filtration rate.

Table 2. Five-year rate of incidence of impaired renal function per 100 person-years by cohort and prediabetes type.

	Nº of cases	Person-years	Rate of incidence (100 person-years)



COHORT
Prediabetes	88	4,928	1.79 (95%CI: 1.45-2.20)
Normoglycemia	61	3,555	1.72 (95%CI: 1.34-2.21)

PREDIABETES TYPE
HbA1c 5.7- 6.4 %	19	1,359	1.40 (95%CI: 0.89-2.19)
FPG 100-125 mg/dL	22	1,066	2.06(95%CI: 1.36-3.13)
HbA1c 5.7- 6.4 % & FPG 100-125 mg/dL	47	2,503	1.88(95%CI: 1.41-2.50)

Table 3. Association risk of comorbidities and lifestyle factors with risk of IRF development.

	HR	95% CI
Age
30 to 49 years	1.00
50 to 64 years	5.12	(1.85-14.15)
65 to 74 years	16.43	(6.04-44.73)
Sex (male)	1.09	(0.79-1.50)
Smoking status
Active smoker	1. 00
Ex-smoker	0.93	(0.57-1.53)
Never smoker	0.78	(0.47-1.28)
Regular physical activity (no)	0.97	(0.70-1.35)
High-risk alcohol consumption (no)	1.28	(0.74-2.20)
Adherence Mediterranean diet score (no)	0.75	(0.54-1.04)
Daily consumption of fruit or vegetables (no)	1.18	(0.66-2.10)
Metabolic syndrome (no)	1.14	(0.82-1.59)
Waist circumference (cm)	1.39	(0.98-1.98)-
BMI ≥30 (kg/m²)	1.21	(0.87-1.68)
Hypertension, (no)	2.08	(1.38-3.12)
Total cholesterol≥ 250 mg/dL*	1.15	(0.74-1.81)
Low HDL-cholesterol mg/dL*	1.28	(0.86-1.91)
Triglycerides≥200 mg/dL	1.00	(0.68-1.45)
Use of ACEIs or ARBs (no)	1.82	(1.31-2.52)

Model 1 (sex- and age-adjusted)

* HDL-C of <40 mg/dL in men or <50 mg/dL in women

Table 4-Hazard ratio of IRF associated to prediabetes and type of prediabetes using different models

	Model 1		Model 2		Model 3		Model 4
	HR	95%CI	HR	95%CI	HR	95%CI	HR	95%CI
Normoglycemia	ref
Prediabetes	0.89	(0.64-1.24)	0.90	(0.65-1.25)	0.76	(0. 54-1.07)	0.76	(0. 54-1.07)
HbA1c 5.7- 6.4 %	0.71	(0.43 -1.20)	0.72	(0.43 -1.21)	0.68	(0.40 -1.14)	0.68	(0.40 -1.15)
FPG 100-125 mg/dL	1.24	(0.76 -2.03)	1.28	(0.78 -2.11)	1.10	(0.66 -1.82)	1.12	(0.68 -1.85)
HbA1c: 5.7- 6.4 % & FPG 100-125 mg/dL	0.87	(0.59 -1.27)	0.87	(0.59 -1.28)	0.69	(0.46 -1.03)	0.68	(0.45 -1.02)

Model 1 (sex- and age-adjusted)

Model 2 (Model 1 plus adjusted by lifestyle variables (i.e. smoking status, regular physical activity, high-risk alcohol consumption, adherence Mediterranean diet (MedDiet) score, and daily consumption of fruit or vegetables)

Model 3 (Model 2 plus adjusted by metabolic risk factors (i.e. Waist circumference, BMI, hypertension, total cholesterol, HDL-cholesterol, triglycerides s)

Model 4 (Model 3 plus adjusted by use of ACEIs or ARBs)

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Heterogeneity in the Association Between Prediabetes Categories and Development of Impaired Renal Function in A 5-Year Follow-Up

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