Biomarkers of inflammatory status in patients with severe obesity before and after bariatric surgery

doi:10.21203/rs.3.rs-5285526/v1

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Biomarkers of inflammatory status in patients with severe obesity before and after bariatric surgery

https://doi.org/10.21203/rs.3.rs-5285526/v1

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Introduction

: Obesity is recognized as a multifactorial chronic disease that has been associated with a chronic inflammatory status.

Objective

To analyze a series of inflammatory parameters in the pre- and postoperative period after bariatric surgery in order to assess the evolution of the inflammatory status before and after surgery.

Method

Observational study that included all patients undergoing bariatric surgery in a university hospital between 2013 and 2023. The variables sex, age, comorbidities, anthropometric parameters, surgical procedure performed, postoperative complications, inflammatory markers neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic inflammation response index (SIRI) and systemic immune-inflammation index (SII), were obtained before surgery and after 6 months, 1, 3 and 5 years. Pre- and postoperative changes observed in these variables after bariatric surgery were analyzed.

Results

A final sample of 386 patients was included in the study, 140 (36.3%) men and 246 (63.7%) women, mean age 45.6 years (SD ± 9.6); 218 (56,5%) sleeve gastrectomy and 168 (43,5%) Roux-en-Y gastric bypass. Pre-surgery, BMI was statistically significantly related to the inflammatory markers NLR (p = 0.003), PLR (p = 0.041), baseline SIRI (p = 0.010) and baseline SIII (p = 0.003) and preoperative baseline weight was related to NLR (p = 0.003) and baseline SIII (p = 0.003). Postoperatively, the observed differences in weight and BMI were also significantly associated with changes in inflammatory marker levels 3 years later.

Conclusions

In patients with severe obesity, weight and BMI show an important relationship with inflammatory parameters. After bariatric surgery, the weight and BMI loss response are associated with a decrease in inflammatory markers.

Obesity. Bariatric surgery. Inflammatory markers. Neutrophil-lymphocyte ratio (NLR). Platelet-lymphocyte ratio (PLR). Systemic Inflammation Response Index (SIRI). Systemic Immune-Inflammation Index (SII)

1. Patients with severe obesity have a chronic inflammatory status that is reflected by changes in diverse biomarkers such as NLR, PLR, SIRI and SII.

2. Most biomarkers of inflammatory status are related to both weight and BMI, pre- and postoperatively.

3. After bariatric surgery, the inflammatory status improves, and a significant relationship is found between the values of these markers and the amount of weight loss and BMI.

Patients with obesity have a status of chronic low-grade inflammation [1, 2] which is associated with vascular and metabolic dysfunction, capable of generating cardiovascular comorbidities, diabetes mellitus and even neoplasms that increase the risk of mortality in these patients [3, 4].

A number of markers have been described that can provide indirect information on this situation, many of which are easily obtained in peripheral blood, such as the

neutrophil/lymphocyte ratio (NLR) [5], platelet/lymphocyte ratio (PLR) [6] or C-reactive protein (CRP) [7]; also some indexes such as the Systemic Inflammation Response Index (SIRI) [8] or the Systemic Immune-Inflammation Index (SII) [9].

There is evidence that patients with obesity may have alterations in inflammatory markers, especially those with comorbidities such as type 2 diabetes, hypertension or hyperlipidemia [6, 10]. However, the response of the proinflammatory status of obesity after patients undergo bariatric surgery (BS) has not been well studied.

The aim of this study was to analyze the evolution of a series of inflammatory markers and their relationship with the degree of obesity before and after bariatric surgery.

Study Design and Population

Observational, longitudinal, and retrospective study of patients who underwent bariatric surgery between 2013 and 2023 in a university hospital. All patients had multidisciplinary evaluation and preoperative study of upper gastrointestinal endoscopy, abdominal ultrasound, blood tests, chest x-ray, spirometry and electrocardiogram.

Inclusion and Exclusion Criteria

Inclusion criteria for bariatric surgery were age 18–60 years, body mass index (BMI) > 40 or > 35 with significant comorbidity. Exclusion criteria were significant psychiatric or eating disorders and substance abuse. Patients with incomplete medical history, techniques other than sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) and re-interventions were excluded from the study.

Surgical Procedure

The procedures were laparoscopic and performed by bariatric surgeons. The standardized surgical technique for RYGB included a 25-30cc gastric pouch with a linear mechanical side-to-side jejunal-jejunal anastomosis and a side-to-side antecolic gastrojejunostomy, leaving a 200 cm alimentary limb and a 70 cm biliopancreatic limb. The gastric sleeve was created over a 40-Fr calibration bougie using a 60-mm linear stapler for gastric division, starting 4cm form the pylorus, up to the angle of His.

RYGB was preferred in patients with BMI > 40, metabolic syndrome, or reflux esophagitis; and SG was preferred in patients with malabsorption, iron deficiency anemia, inflammatory bowel disease, diarrhea syndrome, severe liver disease, immunosuppressive treatment, extreme age, or near pregnancy.

Follow-up

Postoperative clinical and analytical follow-up of the patients was performed at 1, 3, 6, 9, 12, 18 and 24 months, and annually thereafter. Loss to follow-up was defined as non-attendance for more than 24 months.

Data Collection and Definitions

Data were extracted from institutional medical records.

Baseline variables were collected: age, sex, comorbidity (Charlson index) [11], ASA classification [12, 13].

For anthropometric variables, weight (kg) and BMI were used, whose formula is weight in Kilograms divided by the square of Height in meters.

In blood counts extracted at different time intervals, the number of neutrophils, lymphocytes, monocytes and platelets were collected, and the following inflammatory markers were calculated:

Neutrophil-lymphocyte ratio (NLR): ratio of neutrophils to lymphocytes [5].
Platelet-lymphocyte ratio (PLR): ratio of platelets to lymphocytes [6].
Systemic Inflammation Response Index (SIRI) [8]: neutrophils x monocytes / lymphocytes.
Systemic Immune-Inflammation Index (SII) [9]: neutrophils x platelets / lymphocytes.

Surgical data such as bariatric technique (RYGB vs SG), hospital stay, reoperation and mortality during admission or up to 30 days postoperatively were recorded. Postoperative complications were categorized into grades I-II (minor complications) vs grades III-IV (major complications) following the Clavien-Dindo classification [14, 15].

Output Variables

Postoperative weight and BMI, and postoperative inflammatory markers (NLR, PLR, SIRI and SIII) were recorded as output variables and collected at 6-month, 1, 3 and 5-year intervals during follow-up. Weight loss was calculated as the difference in weight and BMI at different intervals after surgery.

Ethics

The study followed the tenets of the Declaration of Helsinki and its updates. Due to its retrospective nature, informed consent of the patients was not required. It was approved by the Center's Clinical Research and Ethics Committee (code 2023-398-1).

Statistical analysis

Next, univariate analysis was performed to determine whether baseline weight and baseline BMI were associated with preoperative markers of inflammation. Pearson's correlation test or Spearman's test was used to compare numerical variables, depending on whether they were parametric or non-parametric variables. The next step was to perform a comparative analysis of the results obtained for weight, BMI and each of the inflammatory markers in each patient preoperatively and postoperatively at 6 months, 1, 3 and 5 years during the follow-up period. The Friedman test for paired data with non-normally distributions was used for this purpose.

Finally, the possible correlation between differences in weight and BMI with the inflammatory biomarkers 3 years after surgery was analyzed.

A p value < 0.05 was considered statistically significant.

Baseline Characteristics

Of the 425 patients initially included in the study, 39 were excluded due to incomplete data, leaving 386 patients; 140 (36.3%) men and 246 (63.7%) women, mean age 45.6 years (SD ± 9.6). The median age-adjusted Charlson Index was 2.0 (RIQ: 1.0–3.0). Regarding surgical risk, 4 (1.0%) patients were ASA I, 115 (29.8%) patients ASA II, 266 (68.9%) patients ASA III and 1 (0.3%) patient ASA IV.

SG was performed in 218 (56.5%) patients and RYGB in 168 (43.5%). In 308 (79.8%) there were no complications; in 56 (14.5%) cases there were mild complications (Clavien-Dindo grades I-II) and in 21 (5.4%) patients there were moderate-severe complications (grades III-IV). The fistula rate was 2.3% (9 patients). Twenty-four (6.2%) were reoperated. There was no operative mortality. The mean length of stay was 4.9 days (SD ± 8.6), median 3 days (RIQ 3.0–5.0).

Weight loss and decrease in BMI

Median baseline weight went from 135 kg (IQR: 117.9–152.3) to 93 kg (IQR: 77.0-109.0) at 5 years after the intervention (p < 0.001). The median baseline BMI went from 47.3 kg/m2 (IQR: 42.8–53.0) to 33.1 kg/m2 (IQR: 29.1–39.2) at 5 years (p < 0.001). In terms of weight loss, the difference between baseline weight and weight at 3 years was an average of 43.4 (± 20.8) kg. On the other hand, the difference between baseline BMI and BMI after 3 years was an average of 15.5 (± 7.1) kg/m².

Associations of Anthropometric Parameters with Inflammatory Biomarkers

a) Baseline status

Univariate analysis showed that baseline weight was statistically significantly related to baseline NLR (p = 0.003) and baseline SIRI (p < 0.001) (Fig. 1). BMI was statistically significantly related to baseline NLR (p = 0.003), baseline PLR (p = 0.041), baseline SIRI (p = 0.010) and baseline SIII (p = 0.003) (Fig. 2).

b) Evolution of the anthropometric data and the inflammatory markers

The evolution of the median values of the anthropometric data and the inflammatory markers over 5 years of follow-up is shown in Fig. 3. We observed that both the anthropometric parameters and the different inflammatory markers decreased in a statistically significant way over the years in the postoperative period. However, a clear trend towards stabilization was observed after 3 years.

c) Correlations of weight loss and BMI decrease with inflammatory markers

Figures 4 and 5 show the correlations between weight loss and BMI decrease with the different inflammatory markers at 3 years after surgery. We observed that both weight loss and BMI loss 3 years after surgery were statistically significantly associated in all cases. Levels of inflammatory markers decreased with greater weight and BMI loss.

Confounding Variables

The possible confounding variables diabetes mellitus, surgical procedure, and postoperative complications were not statistically significantly associated with inflammatory markers at 3 years (Table 1).

Table 1

Univariate analysis of the variables surgical procedure and postoperative complications with inflammatory markers at 3 years after surgery (U-Mann-Whitney test).
		NLR 3 years Median (IQR) (p value)	PLR 3 years Median (IQR) (p value)	SIRI 3 years Median (IQR) (p value)	SIII 3 years Median (IQR) (p value)
Diabetes Mellitus No Yes (p value)		1.7 (1.3–2.3) 1.7 (1.2–2.2) 0.860	112.0 (91.4-141.7) 101.7 (80.3–138.0) 0.141	0.8 (0.6–1.2) 0.8 (0.5–1.1) 0.854	395.3 (300.0-529.3) 385.3 (277.1-196.2) 0.516
Surgical procedure: Sleeve Gastrectomy Gastric Bypass (p value)		1.7 (1.2–2.1) 1.7 (1.3–2.3) 0.266	107.2 (82.3–143.0) 107.4 (83.8-137.1 0.791	0.8 (0.6-1.0) 0.8 (0.6–1.4) 0.097	394.0 (273.7-525.1) 392.6 (293.4-578.4) 0.528
Postoperative complications: CD 0 CD I-II CD III-IV (p value)	1.7 (1.2–2.2) 1.8 (1.2–2.3) 1.4 (1.3–2.1) 0.564		11.5 (83.8-143.1) 101.2 (77.8-123.2) 101.5 (91.7-137.4) 0.369	0.8 (0.6–1.1) 0.9 (0.6–1.4) 0.8 (0.6–1.2) 0.496	392.8 (278.4-526.9) 387.8 (285.0-595.3) 421.3 (350.5-434.4) 0.888
^{CD: Clavien−Dindo score}

This is the first study to analyze the response of the inflammatory status associated with obesity after weight loss following bariatric surgery (SG and RYGB), measured by blood values of the NLR, PLR, SIRI, SIII indices. These indices, which are easy to obtain, could act as markers of the immunological/inflammatory status with prognostic character in different pathologies [16, 17, 18, 19] and predictors of outcome after several surgical procedures, including bariatric surgery [20, 21, 22].

The NLR is one of the most recently studied indices and has been shown to be a predictor of adverse outcomes in patients with cardiovascular, neoplastic, and inflammatory disease [23], and has been found to be associated with other proinflammatory biomarkers, adiposity, and progressive subclinical atherogenesis [24]. This marker may reflect the balance between the innate (neutrophils) and adaptive (lymphocytes) immune response [23]. Neutrophils mediate the inflammatory response through numerous biochemical mechanisms (release of arachidonic acid metabolites, platelet aggregating factors, free radicals, hydrolytic enzymes) [25], and regulatory T lymphocytes play an inhibitory role in atherosclerosis [26]. NLR reflects the neutrophilia of inflammation and the relative lymphopenia of the cortisol-induced stress response [25]. It is a more stable measure than single cell counts [26] because it is not affected by cortisol production or neuroendocrine stress [27, 28].

Obesity increases the development of lymphopenia and neutrophilia, increasing the NLR [29]. Our results, in agreement with those of other authors [30], show that the higher the BMI, the higher the NLR. Herishanu et al. found an association between leukocytosis and BMI > 30 kg/m2, with an 11% increased risk of leukocytosis per 1 kg/m2 increase in BMI [31].

The NLR has been suggested as an independent predictor of metabolic syndrome and cardiovascular disease [32], being related, in addition to obesity, with hypertension, hyperlipemia, and diabetes incidence, severity, and control [10, 33, 34]. A correlation between the number of metabolic syndrome criteria and the increase in NLR is even described [30, 35].

Although reference values for NLR have not yet been standardized, especially in patients with obesity, NLR levels between 0.78 and 3.53 have been described in the healthy population [36]. Hashemi et al. established NLR > 2.12 as a risk cut-off value for patients with central obesity and development of metabolic syndrome [10]. The mean NLR in patients with obesity who were candidates for bariatric surgery in our study was 2 (1.5–2.7) and decreased to 1.6 (1.2–2.1) at 5 years after bariatric surgery. Imtiaz et al. observed a significant association between NLR and the probability of having arterial hypertension in patients with NLR greater than 2.57, showing that type 2 diabetes developed when the index exceeded 3.12 [37].

Regarding the incidence of complications after bariatric surgery, the association with the initial NLR is not clear and, like other authors [38, 39], we found no association. A recent meta-analysis found only temporary changes and in certain subgroups of complications compared to CRP and NLR levels. [40].

Concerning the outcomes of bariatric surgery, we observed a decrease in the values of NLR, PLR, SII and SIRI along with a decrease in postoperative weight and BMI (p < 0.001). The preoperative NLR has been proposed as a predictive marker of weight loss and improvement of diabetes after BC [16]. Bulur O et al. found a decrease in NLR levels at 3, 6 and 12 months after SG (p < 0.001) [41]. A retrospective study of 100 patients with SG found a significant decrease in NLR from 2.21 (baseline) to 1.78 at 3 months postoperatively. A preoperative NLR < 2.36 had a sensitivity of 67.6% and a specificity of 62.5% for predicting successful weight loss 3 months after SG [42]. An extension of this study concludes that high BMI, NLR ≥ 2.36, and female gender are risk factors for predicting suboptimal excess weight loss (EBWL < 37.7%) at 3 months after SG [43].

The PLR was found to be increased in patients with obesity [6]. In our case, it was statistically significantly associated with baseline BMI. The usefulness of PLR and plateletcrit as markers of increased thrombotic status and inflammatory response in patients with severe obesity is suggested [6].

SIRI and SII may serve to detect and predict the presence and severity of systemic inflammatory processes, including cardiovascular disease and metabolic syndrome [44]. In our study, we found an association of SIRI and SII with both baseline BMI and post-bariatric surgery BMI, with variations according to gender and age.

In line with our results, other authors find that bariatric surgery could have an effect of improving the inflammatory profile and increasing anti-inflammatory markers [45]. In this sense, our study goes one step further by providing evidence in this direction.

Limitations

The main limitations of this study are those derived from its retrospective design, with a long follow-up period, due to the loss of data from some older patients. However, it is a sample with a significant number of patients, treated by the same surgical team, with homogeneity of management and whose results can be extrapolated to the general population.

The main findings of this study showed that:

1) In patients with severe obesity there is a chronic inflammatory status that is reflected in the values of certain biomarkers. Most of the biomarkers of inflammatory status are related to both weight and BMI.

2) After bariatric surgery, this inflammatory status improves considerably. The inflammatory biomarkers progressively decrease significantly during follow-up. However, a plateau is observed after 3 years.

3) The biomarkers of inflammatory status after surgery are statistically significantly related to the amount of weight loss and BMI.

Funding Declaration

No funding was received for this study.

Pararasa C, Bailey CJ, Griffiths HR. Ageing, adipose tissue, fatty acids and inflammation. Biogerontology. 2015;16(2):235-48.
Andersen CJ, Murphy KE, Fernandez ML. Impact of Obesity and Metabolic Syndrome on Immunity. Adv Nutr. 2016;7(1):66-75.
Fruh SM. Obesity: Risk factors, complications, and strategies for sustainable long-term weight management. J Am Assoc Nurse Pract. 2017;29(S1):S3-S14.
NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627-42.
Martínez-Urbistondo D, Beltrán A, Beloqui O, Huerta A. The neutrophil-to-lymphocyte ratio as a marker of systemic endothelial dysfunction in asymptomatic subjects. Nefrologia. 2016;36(4):397-403.
Erdal E, İnanir M. Platelet-to-lymphocyte ratio (PLR) and Plateletcrit (PCT) in young patients with morbid obesity. Rev Assoc Med Bras (1992). 2019 Oct 10;65(9):1182-7.
Randell EW, Twells LK, Gregory DM, Lester KK, Daneshtalab N, Dillon C, et al. Pre-operative and post-operative changes in CRP and other biomarkers sensitive to inflammatory status in patients with severe obesity undergoing laparoscopic sleeve gastrectomy. Clin Biochem. 2018;52:13-9.
Qi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158-67.
Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20(23):6212-22.
Hashemi Moghanjoughi P, Neshat S, Rezaei A, Heshmat-Ghahdarijani K. Is the Neutrophil-to-Lymphocyte Ratio an Exceptional Indicator for Metabolic Syndrome Disease and Outcomes? Endocr Pract. 2022;28(3):342-8.
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245-51.
American Society of Anesthesiologists. New classification of physical status. Anesthesiology 1963; 24:111.
Horvath B, Kloesel B, Todd MM, Cole DJ, Prielipp RC. The Evolution, Current Value, and Future of the American Society of Anesthesiologists Physical Status Classification System. Anesthesiology. 2021;135(5):904-19.
Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240(2):205-13.
Bolliger M, Kroehnert JA, Molineus F, Kandioler D, Schindl M, Riss P. Experiences with the standardized classification of surgical complications (Clavien-Dindo) in general surgery patients. Eur Surg. 2018;50(6):256-61.
Kashihara H, Shimada M, Yoshikawa K, Tokunaga T, Nakao T, Nishi M, et al. The Impact of the Pre-operative Neutrophil-lymphocyte Ratio as the Predictive Marker of Post-operative Weight Loss and Improving Diabetes in Sleeve Gastrectomy. J Med Invest. 2023;70(1.2):140-4.
Watanabe J, Otani S, Sakamoto T, Arai Y, Hanaki T, Amisaki M, et al. Prognostic indicators based on inflammatory and nutritional factors after pancreaticoduodenectomy for pancreatic cancer. Surg Today. 2016;46(11):1258-67.
Jabłońska B, Pawlicki K, Mrowiec S. Associations between Nutritional and Immune Status and Clinicopathologic Factors in Patients with Pancreatic Cancer: A Comprehensive Analysis. Cancers (Basel). 2021;13(20):5041.
Yamamoto T, Kawada K, Obama K. Inflammation-Related Biomarkers for the Prediction of Prognosis in Colorectal Cancer Patients. Int J Mol Sci. 2021;22(15):8002.
Da Silva M, Cleghorn MC, Elnahas A, Jackson TD, Okrainec A, Quereshy FA. Postoperative day one neutrophil-to-lymphocyte ratio as a predictor of 30-day outcomes in bariatric surgery patients. Surg Endosc. 2017;31(6):2645-50.
Vaughan-Shaw PG, Rees JR, King AT. Neutrophil lymphocyte ratio in outcome prediction after emergency abdominal surgery in the elderly. Int J Surg. 2012;10(3):157-62.
Forget P, Moreau N, Engel H, Cornu O, Boland B, De Kock M, et al. The neutrophil-to-lymphocyte ratio (NLR) after surgery for hip fracture (HF). Arch Gerontol Geriatr. 2015;60(2):366-71.
Azab B, Camacho-Rivera M, Taioli E. Average values and racial differences of neutrophil lymphocyte ratio among a nationally representative sample of United States subjects. PLoS ONE. 2014;9(11):e112361
Suárez-Cuenca JA, Ruíz-Hernández AS, Mendoza-Castañeda AA, Domínguez-Pérez GA, Hernández-Patricio A, Vera-Gómez E, et al. Neutrophil-to-lymphocyte ratio and its relation with pro-inflammatory mediators, visceral adiposity and carotid intima-media thickness in population with obesity. Eur J Clin Invest. 2019;49(5):e13085.
Spark JI, Sarveswaran J, Blest N, Charalabidis P, Asthana S. An elevated neutrophil-lymphocyte ratio independently predicts mortality in chronic critical limb ischemia. J Vasc Surg. 2010;52(3):632-6.
Shah N, Parikh V, Patel N, Patel N, et al. Neutrophil lymphocyte ratio significantly improves the Framingham risk score in prediction of coronary heart disease mortality: Insights from the National Health and Nutrition Examination Survey-III. Int J Cardiol. 2014;171(3):390-7.
Segel GB, Halterman MW, Lichtman MA. The paradox of the neutrophil's role in tissue injury. J Leukoc Biol. 2011;89(3):359-72.
Gennari R, Dominioni L, Imperatori A, Bianchi V, Maroni P, Dionigi R. Alterations in lymphocyte subsets as prognosticators of postoperative infections. Eur J Surg. 1995;161(7):493-9.
Atmaca HU, Akba F, Ötken IN, Nuhoğlu E, Inal BB. Can the neutrophil-to-lymphocyte ratio serve as an inflammatory marker in obesity? Istanbul Med J. 2014;15:216-20.
Fonseca-González GT, Suárez-Cuenca JA, Flores-Alcántar MG, Garro-Almendaro AK. Distribución del índice neutrófilo/linfocito en diferentes fenotipos metabólicos de obesidad. Rev Mex Endocrinol Metab Nutr. 2018;5:13-20.
Herishanu Y, Rogowski O, Polliack A, Marilus R. Leukocytosis in obese individuals: possible link in patients with unexplained persistent neutrophilia. Eur J Haematol. 2006;76(6):516-520.
Ridker PM, Hennekens CH, Buring JE, Rifai N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 2000;342(12):836-43
Yasar Z, Buyuksirin M, Ucsular FD, Kargı A, Erdem F, Talay F, et al. Is an elevated neutrophil-to-lymphocyte ratio a predictor of metabolic syndrome in patients with chronic obstructive pulmonary disease? Eur Rev Med Pharmacol Sci. 2015;19(6):956-62.
Syauqy A, Hsu CY, Rau HH, Chao JC. Association of dietary patterns, anthropometric measurements, and metabolic parameters with C-reactive protein and neutrophil-to-lymphocyte ratio in middle-aged and older adults with metabolic syndrome in Taiwan: a cross-sectional study. Nutr J. 2018;17(1):106.
Bahadır A, Baltacı D, Türker Y, Türker Y, Iliev D, Öztürk S, et al. Is the neutrophil-to-lymphocyte ratio indicative of inflammatory state in patients with obesity and metabolic syndrome? Anatol J Cardiol. 2015;15:816-22.
Forget P, Khalifa C, Defour JP, Latinne D, Van Pel MC, De Kock M. What is the normal value of the neutrophil-to-lymphocyte ratio? BMC Res Notes. 2017;10(1):12.
Imtiaz F, Shafique K, Mirza SS, Ayoob Z, Vart P, Rao S. Neutrophil lymphocyte ratio as a measure of systemic inflammation in prevalent chronic diseases in Asian population. Int Arch Med. 2012;5(1):2.
Dogan F, Dincer M. Mini-gastric Bypass Complications and the Value of the Preoperative Neutrophil to Lymphocyte Ratio in Early Prediction of Complications. J Coll Physicians Surg Pak. 2021;31(1):70-3.
Binboga S, Isiksacan N, Kasapoglu P, Binboga E, Koser M, Cikot M, et al. Plasma presepsin in determining gastric leaks following bariatric surgery. Turk J Biochem. 2019;44(4):565-73.
AziziKia H, Shojaei S, Mousavi A, Salabat D, Shaker F, Dolama RH, et al. Periprocedural Changes of Serum Biomarkers in Predicting Complications Following Bariatric Surgery for Obesity: Systematic Review and Meta-analysis. Obes Surg. 2024;34(6):2198-215.
Bulur O, Öztürk D, Ertuğrul DT, Sayın S, Asiltürk Z, Dal K, et al. Effects of sleeve gastrectomy on neutrophil-lymphocyte ratio. J Basic Clin Physiol Pharmacol. 2021;33(4):471-5.
Chi PJ, Wu KT, Chen PJ, Chen CY, Su YC, Yang CY, et al. The serial changes of Neutrophile-Lymphocyte Ratio and correlation to weight loss after Laparoscopic Sleeve Gastrectomy. Front Surg. 2022;9:939857
Chen JH, Chi PJ, Chen CY, Tai CM, Chen PJ, Su YC, et al. Three-Month Excessive Body Weight Loss < 37.7% as a Predictor of Mid-term Suboptimal Outcomes Postlaparoscopic Sleeve Gastrectomy: Risk Factors and the Impact of Neutrophil-to-Lymphocyte Ratio on Adipocyte Function. Obes Surg. 2024;34(7):2317-28.
Marra A, Bondesan A, Caroli D, Sartorio A. Complete Blood Count (CBC)-Derived Inflammation Indexes Are Useful in Predicting Metabolic Syndrome in Adults with Severe Obesity. J Clin Med. 2024;13(5):1353.
Farias G, Netto BDM, Boritza K, Bettini SC, Vilela RM, Dâmaso AR. Impact of weight loss on inflammation state and endothelial markers among individuals with extreme obesity after gastric bypass surgery: A 2-year follow-up study. Obes Surg. 2020;30(5):1881-90.

No competing interests reported.

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Biomarkers of inflammatory status in patients with severe obesity before and after bariatric surgery

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Version 1

Abstract

Introduction

Objective

Method

Results

Conclusions

Figures

Key points

Introduction

Material and Methods

Study Design and Population

Inclusion and Exclusion Criteria

Surgical Procedure

Follow-up

Data Collection and Definitions

Output Variables

Ethics

Statistical analysis

Results

Baseline Characteristics

Weight loss and decrease in BMI

a) Baseline status

b) Evolution of the anthropometric data and the inflammatory markers

c) Correlations of weight loss and BMI decrease with inflammatory markers

Confounding Variables

Discussion

Limitations

Conclusions

Declarations

References

Additional Declarations

Status:

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