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Enregistrement W6931896131 · doi:10.5281/zenodo.8004600

Effect of Glycemic Control on Urinary Tract Infections in Type 2 Diabetic Mellitus

2023· article· en· W6931896131 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Langueen
DomaineMedicine
ThématiquePrenatal Screening and Diagnostics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDiabetes mellitusGlycemicUrinary systemAsymptomaticType 2 Diabetes MellitusType 2 diabetesUrineAsymptomatic bacteriuriaHemodialysis

Résumé

récupéré en direct d'OpenAlex

Introduction Type 2 Diabetes Mellitus (DM) is frequently associated with increased risk of urinary tract infection [1-4]. Poor metabolic control in diabetes along with impaired immune system, microvascular disease in kidney and diabetic cytopathy contribute to it [5-8]. Severe form of urinary tract infections like emphysematous pyelonephritis is more frequent in diabetics [9]. Bacterial UTI are common in diabetics and needs aggressive treatment [10]. E. coli is the most common organism causing UTI, other pathogens that are highly prevalent in diabetics are Klebsiella, Enterococci, Pseudomonas, and Proteus mirabilis, group B Streptococci and fungal infections [11,12]. Improved glycemic control in diabetic cases helps in controlling UTI and proper and accurate screening for UTI in diabetics helps in avoiding complications [13]. There is limited information on glycemic control and UTI in India, therefore we aim to evaluate the effect of glycemic control on UTI in Type 2 Diabetes patients. Materials and Methods This is a retrospective study that included patients reporting to the Endocrinology and Urology Out Patient Department (OPD) with type 2 diabetes mellitus and symptomatic UTI from January 2021 to October 2022. Type 1 DM, pancreatic diabetes, steroid induced diabetes, and other types of diabetes were excluded. Further patients sterile on urine culture, pregnant female, patients with asymptomatic bacteriuria, patients onper urethral catheter and patients on maintenance hemodialysis were excluded. Information like patient’s age, gender, relevant history, examination, laboratory report and imaging finding were collected from OPD record. Body mass index (BMI, Kg/M2) was calculated by height and weight measurement. The plasma glucose was measured by glucose oxidase method and the HbA1c was measured by Bio-Rad D-10 system using a high-performance liquid chromatography method. DM was diagnosed based on 75-g oral glucose tolerance test (OGTT) and/or glycosylated hemoglobin (HbA1c) or based on record in OPD tickets for previously diagnosed diabetic cases [14]. Patients were divided into two groups based on glycemic control, Group 1: good glycemic control(HbA1C<7%), Group 2: suboptimal glycemic control (HbA1C ≥7%). (14) Midstream urine samples were collected after giving proper instructions. The urine samples were immediately transported to the microbiology laboratory. If the urine specimen was found to be contaminated repeat sample was collected on next day. Smears for Gram's staining, culture and biochemical tests for identifying the species of the pathogens were processed using the standard microbiological procedures. Diagnosis of UTI was made if cultures had >105 colony forming units (CFUs)/mL of a single potential pathogen or two potential pathogens. The presence of yeast in any number was significant. Quantitative variables were expressed as mean±standard deviation and analyzed using independent sample t-test. Qualitative variables were expressed as percentage and was analyzed using Fischer Exact test. P-value <0.05 was considered significant. Results We retrospectively collected and evaluated the data of 156 patients having diagnosis of Type 2 DM with urinary tract infection. Baseline characteristics has been summarized in Table 1. Most common symptoms were dysuria (96.8%), frequency (94.2%) & urgency (84.6%) followed by sense of incomplete voiding (60.8%), fever (55.1%), straining to void (44.9%), abdominal pain (32.7%), urinary incontinence (14.1%) and hematuria (6.4%). Average duration of diabetes was 8.9 ±5.7 years & prevalence of newly diagnosed diabetes was 9.6%. Prevalence of Gram negative, Gram positive and Candida were 71.8, 19.9%& 14.1% respectively (Table 2). In Gram negative, E. coli (67.9%) was most common while in Gram positive, Enterococcus fecalis (70.9%) was most common organism (Table 2). Prevalence of good glycemic control & suboptimal glycemic control was 33.7% & 67.3 % respectively. (Table 3). HbA1C [10.5±1.9% vs 6.1±0.6%, p=0.0001] and random plasma glucose 315±146.7 vs 142±52.6 mg/dL= 0.0001] were significantly more in suboptimal versus good glycemic control. Age [56.2±10.3 vs 45.2±8.2 years, p=0.0001] & White Blood Cell (WBC) [16.8±8.5 vs 13.5±5.6 *103/mm3, p=0.0128] were significantly more in suboptimal glycemic control group versus good glycemic control group. Hemoglobin [9.26±1.9 vs. 10.5±2.2 gm/dL, p=0.0004] and GFR (Glomerular Filtration Rate) [58±25.6vs. 72.2±28.4 ml/minutes/1.73m2, p=0.002] were significantly lower in suboptimal versus good glycemic control group. Acute pyelonephritis was significantly more in suboptimal glycemic control group as compared to good glycemic control group [24.7% vs. 9.8%, p=0.0325]. Cystitis was more common in good versus suboptimal glycemic control but not statistically significant [78.4% vs 67.6%, p=0.19]. Similarly, there was no significant difference in acute prostatitis and emphysematous pyelonephritis in good versus suboptimal glycemic control group (Table 3). Discussion In our study we found increase incidence of UTI in suboptimal glycemic control diabetics as compared to good control diabetics. Previous study in the past have found diabetic women more predisposed to UTI as compared to those not having diabetes [15]. In this study we found E. coli (67.9%) to be the most common Gram-negative organism followed by K. pneumoniae (16.1%) and Psuedomonas aeruginosa (6.3%). E. coli and K. pneumoniae was found to be responsible for about three fourth of gram-negative cases of UTI in Kuwait [13]. Another study found the prevalence of E. coli, K. pneumoniae and P. aeruginosa in 71.3%, 13.5% and 8.8% respectively in type 2 diabetic patients in south India [16]. In Gram positive, we found Enterococcus faecalis was most prevalent (71%) followed by Staphylococcus epidermidis in 22.6 % like another study from south India [16]. We found good glycemic control in one third of the cases and suboptimal glycemic control in two third of cases. Prevalence of good glycemic control has been reported between 13.7 % 2 to 44.8% in different studies in urinary tract infection with diabetic patients [13,16-18]. In our study we found cystitis in 78.4% of cases with good glycemic control as compared to 67.6% of cases of suboptimal glycemic control. Acute prostatitis and emphysematous pyelonephritis were found in 7.8 % and 3.9% respectively in cases of good glycemic control and 3.8% and 3.8%respectively in those having poor glycemic control. Acute pyelonephritis was found in 9.8% of patients with good glycemic control as compared to 24.7% in those with poor glycemic control (p- value 0.0325). In a study by Washington State Health group pyelonephritis was 4.1 times more common in premenopausal diabetic women than in non-diabetic women [19]. Another study reported patients with diabetes mellitus were 3 times more prone to hospitalization for pyelonephritis as compared to those without diabetes [20]. A Canadian study found 6-15 times more hospitalization for diabetic women as compared to non-diabetics and diabetic men needed 3.4-17 times more hospitalization as compared to non-diabetic men [21]. Risk of acute bacterial prostatitis, prostatic abscess has been found to increase in patients of diabetes mellitus [22,23]. In our study we found random blood glucose, HbA1C, age & WBC counts to be significantly higher while hemoglobin and GFR were significantly lower in suboptimal glycemic control group in comparison to good glycemic control group (p-value <0.05). Studies have shown increased blood glucose to be related to higher chances of UTI and bacteriuria [24,25]. On contrary to this, one meta-analysis and systemic review showed that increased blood glucose level was not a significant factor for UTI in diabetic patients [26]. A study done in Type 2 diabetes mellitus in females found age more than 40 years is an important risk factor for UTI [27]. Higher HbA1C has been shown to be strongly associated with risk of CKD [28]. The limitations of our study are retrospective nature, single centre study, small sample size, confounding factor like sex was not analyzed, follow up was not included. Conclusion In type 2 diabetes mellitus, acute pyelonephritis was more common in suboptimal glycemic control group in comparison to good glycemic control. E. coli and Enterococcus fecalis was most common organism in Gram negative and Grampositive bacteria respectively. Age & WBC counts were significantly higher while Hemoglobin and GFR were significantly lower in suboptimal glycemic control group in comparison to good glycemic control group. References 1. Patterson JE, Andriole VT. Bacterial urinary tract infections in diabetes. Infect Dis Clin North Am. 1997 Sep;11(3):735– 50. 2. Joshi N, Caputo GM, Weitekamp MR, Karchmer AW. Infections in patients with diabetes mellitus. N Engl J Med. 1999 Dec 16;341(25):1906–12. 3. Boyko EJ, Fihn SD, Scholes D, Abraham L, Monsey B. Risk of urinary tract infection and asymptomatic bacteriuria among diabetic and nondiabetic postme

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,276
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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