Sage Journals: Discover world-class research

Abstract

Background:

Abdominal obesity (AO) is associated with hyperuricemia (HU) in people with type 2 diabetes (T2DM), but most evidence comes from Asian studies and Latin American data are scarce. This study addresses that gap in a Peruvian setting. We aimed to evaluate the association between AO and HU in adult Peruvians diagnosed with T2DM.

Methods:

We carried out a cross-sectional observational study in adults with T2DM in a private clinic in Lima – Peru. We defined HU as serum uric acid ⩾ 6 mg/dL and defined AO as waist circumference ⩾ 94 cm in men and ⩾88 cm in women. In addition, we estimated crude and adjusted prevalence ratios (PRs) with 95% CIs for the AO–HU association, adjusting for sex, glycosylated hemoglobin, estimated glomerular filtration rate, and systolic/diastolic blood pressure.

Results:

We evaluated 815 adults with T2DM. 62.6% were male, and the average age was 57 years. AO was present in 82.9% and HU in 22.7%. The prevalence of HU in adults with AO was 24.3%, while the prevalence was 15.1% in those not obese. AO was associated with HU in the crude analysis (cPR: 1.60 95% CI 1.05-2.43) and remained statistically significant after adjustment for sex, glycosylated hemoglobin, estimated glomerular filtration rate, and systolic/diastolic blood pressure (aPR: 1.95 95% CI 1.31-2.90).

Conclusion:

In Peruvian adults with T2DM, abdominal obesity was associated with nearly double the prevalence of hyperuricemia. Because waist circumference is simple and low-cost to measure, integrating it into routine diabetes visits, along with targeted uric acid screening and management, may enhance cardiometabolic risk stratification and guide timely interventions.

Plain Language Summary

Exploring the Connection Between Abdominal Obesity and Elevated Uric Acid in Peruvian Adults with Type 2 Diabetes

People with type 2 diabetes often gain fat around the waist, sometimes called ‘belly fat’. This type of fat can raise uric acid levels in the blood, which may lead to gout and other health problems. Most research connecting belly fat and high uric acid has focused on Asian populations, so we wanted to understand whether this pattern also appears in Latin America. We reviewed information from 815 adults with type 2 diabetes who received care in Lima, Peru, noting each person’s waist size and uric acid test. Many had a large waist, and a sizeable number had high uric acid. Those with more fat around the middle were about twice as likely to have elevated uric acid as those without it, even after we accounted for sex, and other health factors. For Peruvians living with type 2 diabetes, a growing waistline seems to be a clear warning sign for high uric acid. Paying attention to waist size, and taking practical steps like healthy eating, regular physical activity, weight management, and seeking medical support when needed, may help lower uric acid and reduce related health risks.

Keywords

uric acid waist circumference obesity abdominal diabetes mellitus type 2

Introduction

Obesity is a global public health problem due to its high prevalence and associated comorbidities.¹ In 2016, the World Health Organization (WHO) estimated a global prevalence of 13%, with projections exceeding one-fifth of the population by 2030.¹ In 2017, Mexico and Bolivia had the highest obesity prevalence in Latin America (32.2% each).² In Peru, prevalence was 19.7% in 2019, with 2030 projections similar to global estimates.³ Obesity is linked to multiple noncommunicable diseases, including hypertension, dyslipidemia, nonalcoholic fatty liver disease, type 2 diabetes mellitus (T2DM), and hyperuricemia (HU).⁴ Because obesity clusters with T2DM and other cardiometabolic conditions, clarifying its relationship with HU is clinically relevant for risk stratification and care planning.

HU is common in cardiometabolic conditions, and its prevalence increases with adiposity. This overlap motivates examination of how adiposity influences urate metabolism. Like obesity, HU is associated with several noncommunicable chronic diseases,^5,6 and although multiple determinants contribute to HU, adiposity is a principal risk factor.^7
-10 The pathophysiology linking obesity to HU remains incompletely understood¹¹; proposed mechanisms include greater exogenous purine intake, increased endogenous purine production, and reduced renal urate excretion.¹² In Peru, population-based estimates of HU are scarce; available reports are largely limited to clinical cohorts, with prevalence around 23.2% among patients with T2DM and 37.9% among those with cardiorenal involvement.^13,14

Individuals with T2DM often present coexisting obesity and HU, conditions linked by complex, and possibly bidirectional, pathophysiologic pathways.^6,15
-17 Although the mechanisms are not fully elucidated, adipose tissue expansion in obesity, together with hyperinsulinemia, appears to perturb uric acid metabolism.^10,11 Adipokines (also termed adipocytokines), bioactive mediators produced by adipose tissue, including adiponectin, leptin, resistin, TNF-α, and IL-6, regulate energy balance, insulin sensitivity, and inflammation, and may influence both uric-acid production and renal excretion¹⁸

Evidence on the obesity–HU association comes largely from Asian cohorts,^16,17,19 with limited evaluation in Hispanic populations, where waist circumference (WC) cutoffs for abdominal obesity (AO) differ from other ethnic groups, as recommended by the Latin American Diabetes Association.²⁰ Moreover, many prior studies defined obesity by body mass index (BMI), whereas WC is a superior predictor of cardiometabolic risk.^21,22 In Peru, the prevalence of AO (38.9%) markedly exceeds BMI-defined obesity (23.2%), underscoring the relevance of WC-based definitions.²³ Accordingly, we aimed to evaluate the association between AO and HU among Hispanic adults with T2DM in Lima, Peru.

Methods

Study Design

We conducted a cross-sectional analytical observational study through a secondary analysis of data from a previously published study. The secondary analysis was carried out between November and December 2020 using fully de-identified electronic health records from Clínica Internacional (Lima, Peru), a private clinic. The records analyzed correspond to health records belonging to the year 2018, as part of the primary study that evaluated the association between glycemic control and albuminuria in patients with T2DM.²⁴

Population, Sample, and Statistical Power

We collected data using non-probability (convenience) sampling. The target population comprised patients who attended the ‘Take Care’ Health Program at Clínica Internacional (Lima, Peru) in 2018. ‘Take Care’ is a disease-management program for individuals with diabetes mellitus, bronchial asthma, arterial hypertension, and dyslipidemia. Benefits provided through patients’ insurance policies include laboratory tests, procedures, and medical consultations according to patient comorbidities. Clinic staff record all patient information in the program database to enable personalized follow-up and appropriate treatment.

We included adults ⩾ 18 years with physician-diagnosed T2DM. Exclusion criteria were current allopurinol use and incomplete key data. The initial sample comprised 907 patients; 86 were excluded for allopurinol use and 6 for missing data, leaving 815 participants for analysis (Figure 1). A sample of 815 provided ≈81% power to detect prevalence ratios ⩾1.6 at a two-sided α = .05. Power calculations were performed using Epidat v4.2

Figure 1.

Participant selection flowchart.

Variables and Measurements

The dependent variable was HU, defined as serum uric acid (UA) ⩾ 6 mg/dL, measured using a colorimetric method, following Kidney Disease: Improving Global Outcomes (KDIGO) guidance.²⁵

The independent variable was AO, defined as waist circumference (WC) ⩾94 cm in men and ⩾88 cm in women, per the Latin American Diabetes Association.¹⁸ WC was measured by trained health professionals using a non-stretchable tape at end-expiration, with participants standing, abdomen relaxed, and the tape positioned horizontally at the umbilical level, ensuring no skin compression.

Covariates included age (years); sex (male/female); glycated hemoglobin (HbA1c, %); fasting plasma glucose (mg/dL); serum creatinine (mg/dL); 24-hour urine albumin (mg/dL); estimated glomerular filtration rate (eGFR) calculated with the Cockcroft–Gault formula (mL/min/1.73 m²); and systolic and diastolic blood pressure (mmHg). All tests formed part of the program’s annual evaluation and were obtained after an overnight (⩾8-hour) fast.

Statistical Analysis

We conducted all analyses in Stata v16 (StataCorp LLC, College Station, TX, USA). Continuous variables were summarized as mean (SD) or median (IQR) according to distribution, and categorical variables as n (%). Distributional assumptions were examined visually and with the Shapiro–Wilk test. Group comparisons by HU and by AO status used Student’s t test for normally distributed continuous variables, Mann–Whitney U for skewed variables, and χ² or Fisher’s exact tests for categorical variables, as appropriate.

We made a multivariate analysis to determine the association between AO and HU. Confounding variables were added using a generalized linear model of the Poisson family with robust variance. Variables from the crude analysis with P-values <.2 were entered into the adjusted model. Using P < .20 at the bivariate screening stage follows the purposeful selection approach to avoid excluding potential confounders that might not meet a stricter threshold (P < .05) but can still materially influence the exposure–outcome estimate. In practice, this reduces residual confounding and the risk of type II error when deciding on adjustments. Collinearity relationships between variables were verified using the variance inflation factor (VIF).

Ethical Aspects

The study protocol was approved by the Ethics Committee of Universidad Peruana de Ciencias Aplicadas (CEI 527-10-20 PI 221-20). This is a secondary analysis of a primary study approved by the same committee (CEI 023-03-20-PI 113-18). The clinic from which data were obtained is not affiliated with the university; for the primary study, the clinic provided formal authorization to use de-identified electronic health records for research (Authorization Letter No. 006-TI-D-UDID-CI-2019). No author is employed by or affiliated with the source clinic, and none has previously held such roles or conducted research on its behalf. Data access was granted under a data-use agreement, and the research team analyzed a fully de-identified dataset. Given the retrospective design and de-identification, informed consent was waived. No direct patient contact occurred. All procedures complied with the Declaration of Helsinki and applicable national regulations.

Results

General Characteristics of the Population

We analyzed 815 patients, of which 62.6% were male with an average age of 57 years. The mean HbA1c was 6.7%, and the median eGFR was 109.2 ml/min/1.73 m². Of the patients evaluated, 22.7% presented HU and 83% AO. The rest of the characteristics are shown in Table 1.

Table 1.

General Characteristics of Study Population.

Variable	Total (n = 815)
Abdominal obesity
Yes	676 (82.9)
No	139 (17.1)
Hyperuricemia
No	630 (77.3)
Yes	185 (22.7)
Age (years)	57 (12.3)
Sex
Female	305 (37.4)
Male	510 (62.6)
Fasting serum glucose (mg/dL)	124 (107-155)
HbA1c (%)	6.7 (6.0-8.2)
24-h urine albumin (mg/dL)	12.9 (5.75-26.8)
Serum creatinine (mg/dL)	0.78 (0.7-0.9)
eGFR (ml/min/1.73 m²)	109.2 (86.4-138.3)
SBP (mmHg)	117.5 (10.5)
DBP (mmHg)	72.1 (7.8)

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, blood glycosylated hemoglobin; SBP, systolic blood pressure.

Mean (Standard deviation), Median (Interquartile range), and Number (Percentage).

Bivariate Analysis

We made 2 bivariate analyzes separately of the factors associated with AO and HU. In the first bivariate analysis, the factors associated with AO were HU (P = .019), fasting glucose (P < .001), HbA1c (P = .025), albuminuria (P < .001), eGFR (P < .001), SBP (P < .001) and DBP (P < .001; Table 2). In the second bivariate analysis, the factors associated with HU were AO (P = .019); sex (P < .001), fasting glucose (P < .001), HbA1c (P < .001), creatinine (P < .001), eGFR (P < .001), and SBP (P = .016; Table 3).

Table 2.

Bivariate Analysis According to Abdominal Obesity Among the Participants.

Variable	No abdominal obesity (n = 139)	Abdominal obesity (n = 676)	P-value
Hyperuricemia			.019
No	118 (84.9)	512 (75.7)
Yes	21 (15.1)	164 (24.2)
Age (years)	58.6 (12.86)	56.7 (12.13)	.098
Sex			.561
Female	49 (35.3)	256 (37.9)
Male	90 (64.7)	420 (62.1)
Fasting serum glucose (mg/dL)	115 (102-133)	126 (108-160)	<.001
HbA1c (%)	6.4 (6-7.5)	6.8 (6.1-8.3)	.025
24-h urine albumin (mg/dL)	9.27 (4.7-17.1)	13.7 (6.4-31)	<.001
Serum creatinine (mg/dL)	0.79 (0.69-0.93)	0.78 (0.67-0.94)	.804
eGFR (ml/min/1.73 m²)	87.5 (72-108.9)	116 (93-142.6)	<.001
SBP (mmHg)	114.1 (10.6)	118.2 (10.3)	<.001
DBP (mmHg)	69.9 (7.8)	72.6 (7.7)	<.001

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, blood glycosylated hemoglobin; SBP, systolic blood pressure.

Mean (Standard deviation), Median (Interquartile range), and Number (Percentage).

Table 3.

Bivariate Analysis According to Hyperuricemia Among the Participants.

Variable	No hyperuricemia (n = 630)	Hyperuricemia (n = 185)	P-value
Abdominal obesity			.019
No	118 (84.9)	21 (15.1)
Yes	512 (75.7)	164 (24.3)
Age (years)	56.9 (12.1)	57.3 (12.8)	.730
Sex			<.001
Female	268 (42.5)	37 (20)
Male	362 (57.5)	148 (80)
Fasting serum glucose (mg/dL)	126 (107-166)	119 (107-137)	<.001
HbA1c (%)	6.8 (6-8.5)	6.6 (6.1-7.1)	<.001
24-h urine albumin (mg/dL)	13 (5.74-27)	12.8 (6-25.3)	.526
Serum creatinine (mg/dL)	0.76 (0.65-0.88)	0.91 (0.78-1.1)	<.001
eGFR (ml/min/1.73 m²)	113 (89.5-138.7)	102.2 (79.1-129.6)	<.001
SBP (mmHg)	117 (10.6)	119.1 (9.7)	.016
DBP (mmHg)	71.9 (7.8)	72.9 (7.8)	.104

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, blood glycosylated hemoglobin; SBP, systolic blood pressure.

Mean (Standard deviation), Median (Interquartile range), and Number (Percentage).

Regression Analysis

In the crude analysis, there was a statistically significant association between AO with HU, male sex, fasting glucose, HbA1c, creatinine, and SBP (Table 4). In the adjusted analysis, the statistically significant association between AO and HU was maintained (PR: 1.95 95% CI 1.31-2.90), with the male sex (PR: 2.45 95% CI 1.77-3.38), eGFR (PR: 0.99 95% CI 0.98-0.99) and HbA1c (PR: 0.76 95% CI 0.69-0.84; Table 4).

Table 4.

Crude and Adjusted Regression Model of Abdominal Obesity and Hyperuricemia Association.

Variable	Crude (95% CI)	P-value	Adjusted (95% CI)	P-value
Abdominal obesity
No	Reference	—	Reference	—
Yes	1.60 (1.05-2.43)	.026	1.85 (1.23-2.76)	<.001
Age (years)	1.00 (0.99-1.01)	.738	—	—
Sex
Female	Reference	—	Reference	—
Male	2.39 (1.71-3.33)	<.001	2.51 (1.81-3.47)	<.001
Fasting serum glucose (mg/dL)^a	0.99 (0.99-0.99)	<.001	—	—
HbA1c (%)	0.79 (0.71-0.86)	<.001	0.76 (0.69-0.84)	<.001
24-h urine albumin (mg/dL)	1.00 (0.99-1.00)	.674	—	—
Serum creatinine (mg/dL)^b	1.80 (1.29-2.48)	<.001	—	—
eGFR (ml/min/1.73 m²)	0.99 (0.99-1.00)	.063	0.99 (0.99-0.99)	.013
SBP (mmHg)	1.01 (1.00-1.02)	.010	1.01 (0.99-1.02)	.088
DBP (mmHg)	1.01 (0.99-1.03)	.102	1.01 (0.99-1.02)	.381

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, blood glycosylated hemoglobin; SBP, systolic blood pressure.

Variable did not enter the adjusted regression model due to collinearity with Hb1Ac.

Variable did not enter the adjusted regression model due to collinearity with GFR.

Discussion

Our principal finding is that AO was independently associated with HU among Peruvian adults with T2DM: participants with AO had nearly twice the prevalence of HU compared with those without AO. We also observed higher HU among men, whereas greater HbA1c and higher eGFR were inversely associated with HU.

In our study, individuals with T2DM and AO had nearly twice the prevalence of HU compared with those without AO, consistent with a Nigerian study reporting an approximately 1.9-fold higher probability of HU among diabetic patients with AO.²⁶ The mechanisms linking AO to HU are multifactorial. Dietary factors likely contribute: consumption of purine-rich foods (eg, red meat, certain seafoods) increases urate production, and high intake of saturated fats may impair renal urate excretion.^8,27,28 Alcohol ingestion elevates serum lactate, which competitively inhibits renal urate transport, thereby reducing urate excretion.^8,29 Fructose (prevalent in sugar-sweetened beverages) induces rapid hepatic ATP depletion with AMP accumulation, which is catabolized to UA via xanthine oxidase, increasing urate generation.^8,30 These pathways may be particularly relevant in Hispanic Latin American settings, where a substantial share of dietary energy comes from carbohydrates, fats, processed foods, and sugar-sweetened beverages.³¹ On the other hand, the role of adipocytes of obese and diabetic people in HU has been suggested.³² The increase in adipose tissue produces an alteration in adipocytokines, increasing leptin and decreasing adiponectin, associated with insulin resistance and subsequent development of T2DM.^33,34 In addition, hyperinsulinemia increases sodium and UA reabsorption in the renal tubules, decreasing urinary UA excretion and consequently causes HU in these patients.^32,35

In our study, male sex was associated with a higher prevalence of HU. Likewise, an Ethiopian study reported that men with T2DM aged ⩾ 45 years had approximately twice the prevalence of HU as women.³⁶ This difference may be partly explained by higher estrogen levels in women of reproductive age: estrogens increase renal urate clearance and inhibit tubular urate reabsorption, conferring protection against HU, an effect that wanes after menopause.³⁷ Estrogen signaling may also modulate urate transporter expression and function, thereby lowering serum UA³⁸; key transporters include ABCG2 and GLUT9/SLC2A9.^39,40 Additional mechanisms likely contribute to the pronounced sex differences in serum UA and remain to be fully elucidated. Moreover, men typically have greater visceral adiposity, a phenotype linked to insulin resistance (as in metabolic syndrome and T2DM) and to elevated serum UA, in part via hyperinsulinemia-mediated reductions in renal urate clearance^5,6

Conversely, higher HbA1c and greater eGFR were associated with a lower prevalence of HU. Our HbA1c result is consistent with findings from Iran and Nigeria.^41,42 A plausible explanation is that glycosuria, when the renal threshold is exceeded, promotes uricosuria by competing with tubular urate reabsorption, thereby lowering serum UA.⁴¹ Although most participants had glucose values below classic glycosuria thresholds, intermittent/postprandial glycosuria or a higher filtered glucose load could exert similar effects; this warrants confirmation in prospective studies. The inverse association with eGFR aligns with prior reports⁴³ and likely reflects greater renal urate clearance at higher eGFR, whereas lower eGFR reduces clearance and favors higher serum UA, even within ranges above conventional chronic kidney disease thresholds.⁴⁴

We recommend implementing AO screening in resource-limited clinics by adding waist circumference to triage vitals at every diabetes visit (or at least quarterly). When AO is documented, staff should provide brief lifestyle counseling (reduce dietary sodium and sugar-sweetened beverages; set physical-activity goals) and review potential contributors (eg, diuretics). Serum UA can be measured annually, or targeted to patients with AO or suboptimal metabolic control.

Our study has limitations. First, the cross-sectional design precludes causal inference and prevents establishing temporality between AO and HU in adults with T2DM. Second, participants were recruited by non-probability (convenience) sampling from a single private clinic in urban Lima during routine care; selection bias is therefore possible. Attendees may differ from the broader Peruvian T2DM population (eg, urban residence, insurance/socioeconomic status, health-seeking behavior, disease severity), and exclusions for missing data may have further shaped the analytic sample. Third, we could not determine the timing of onset of obesity, HU, or T2DM. Fourth, we lacked data on some comorbidities and medications that influence urate metabolism, so residual confounding cannot be excluded. Despite these limitations, to our knowledge this is the first study in a Hispanic T2DM population to evaluate the AO–HU association. Prospective cohorts in diverse settings, with detailed comorbidity and medication data, are warranted to clarify temporality and causation.”

Conclusion

Among adults with T2DM, abdominal obesity was independently associated with a higher prevalence of hyperuricemia after adjustment for sex, age, systolic blood pressure, HbA1c, and eGFR. Integrating waist-circumference screening and brief lifestyle counseling into routine diabetes visits, coupled with annual or targeted uric acid testing, may facilitate earlier detection and follow-up of hyperuricemia, particularly in resource-limited settings

Footnotes

ORCID iDs

Camila Guerreros-Espino

Percy Herrera-Añazco

Vicente A. Benites-Zapata

Ethical Considerations

Ethical approval for this study was obtained from the Ethics Committee from Universidad Peruana de Ciencias Aplicadas with the CEI code 527-10-20 PI 221-20.

Consent to Participate

Informed consent was not sought for the present study because is a secondary analysis of a primary study. The private clinic extracted the data from its electronic records and provided it in a de-identified format. Only the researchers had access to the data.

Consent for Publication

Not applicable.

Author Contributions

F. Peralta-Vera: Conceptualization, Data curation, Methodology, Software, Writing - original draft, Writing - review & editing. Enzo Castillo-Céspedes: Conceptualization, Data curation, Methodology, Software, Writing - original draft, Writing - review & editing. Lucero Collazos-Huamán: Conceptualization, Methodology, Software, Writing - original draft, Writing - review & editing. Camila Guerreros-Espino: Conceptualization, Methodology, Software, Writing - original draft, Writing - review & editing. Percy Herrera-Añazco: Conceptualization, Methodology, Software, Writing - original draft, Writing - review & editing. Vicente Benites-Zapata: Conceptualization, Formal analysis, Methodology, Software, Supervision, Writing - original draft, Writing - review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data are available upon reasonable request to the corresponding author.

References

Malo Serrano

Nancy Castillo

Daniel Pajita

. La obesidad en el mundo. An Fac Med. 2017;78(2):67.

Federación Latinoamericana de Sociedades de Obesidad. II Congreso Latinoamericano. 2017. [Internet][Citado el 19 de Noviembre de 2020] Disponible en: http://www.administracion.usmp.edu.pe/institutoconsumo/wp-content/uploads/LIBRO-II-CONSENSO-LATINOAMERICANO-DE-OBESIDAD-2017.pdf

Pajuelo

Torres

Agüero

Bernui

Sobrepeso y obesidad en la población adulta del Perú. An Fac Med. 2019;80(1):21-27.

Fan

Zheng

, et al. Abdominal obesity is strongly associated with cardiovascular disease and its risk factors in elderly and very elderly community-dwelling Chinese. Sci Rep. 2016;6(1):9.

King

Lanaspa

Jensen

Tolan

Sánchez-Lozada

Johnson

RJ.

Uric acid as a cause of the metabolic syndrome. Contrib Nephrol. 2018;192:88-102.

Meng

, et al. High serum uric acid and increased risk of type 2 diabetes: a systemic review and meta-analysis of prospective cohort studies. PLoS One. 2013;8(2):1-7.

Vargas

Ácido úrico y Síndrome metabólico: Causa o efecto. Arch en Artículo Espec. 2017;19(4):155-169.

Álvarez

Valdivielso

JL.

Hiperuricemia y gota: El papel de la dieta. Nutr Hosp. 2014;29(4):760-770.

Ali

Perveen

Rahman

, et al. Prevalence of hyperuricemia and the relationship between serum uric acid and obesity: a study on Bangladeshi adults. PLoS One. 2018;13(11):1-12.

10.

Cruz-Domínguez

Cortés

Zarate

, et al. Relationship of ghrelin, acid uric and proinflammatory adipocytokines in different degrees of obesity or diabetes. Int J Clin Exp Med. 2014;7(5):1435-1441.

11.

Gong

Wen

Nguyen

Wang

Jin

Zhou

Converging relationships of obesity and hyperuricemia with special reference to metabolic disorders and plausible therapeutic implications. Diabetes Metab Syndr Obes. 2020;13:943-962.

12.

Saag

Choi

Epidemiology, risk factors, and lifestyle modifications for gout. Arthritis Res Ther. 2006;8 Suppl 1(1):S2.

13.

Venero

JEV

Benites-Zapata

VA.

Hyperuricemia as a associated factor to atrial fibrillation in type 2 diabetes mellitus patients an a private clinic in Peru 2020. Metabolism. 2021;116:154623.

14.

Virú-Loza

Palacios-Guillén

AM.

[Hyperuricemia and progression of chronic cardiorenal compromise]. Rev Peru Med Exp Salud Publica. 2014;31(3):530-534 (in Spanish).

15.

Wang

Chen

Lei

Song

Zhang

, et al. Prevalence and determinants of hyperuricemia in type 2 diabetes mellitus patients with central obesity in Guangdong province in china. Asia Pac J Clin Nutr. 2013;22(4):590-598.

16.

Han

Meng

Shan

, et al. Temporal relationship between hyperuricemia and obesity, and its association with future risk of type 2 diabetes. Int J Obes. 2018;42(7):1336-1344.

17.

Kuwabara

Hisatome

, et al. “Metabolically healthy” obesity and hyperuricemia increase risk for hypertension and diabetes: 5-year Japanese cohort study. Obesity. 2017;25(11):1997-2008.

18.

Borges

Franca

Fujimori

, et al. Relationship between proinflammatory cytokines/chemokines and adipokines in serum of young adults with obesity. Endocr Metab Immune Disord Drug Targets. 2018;18(3):260-267.

19.

Kim

Kang

Kim

GT.

Prevalence of hyperuricemia and its associated factors in the general Korean population: an analysis of a population-based nationally representative sample. Clin Rheumatol. 2018;37(9):2529-2538.

20.

Asociación Latinoamericana de Diabetes (ALAD). Consenso Latinoamericano de la Asociación Latinoamericana de Diabetes (ALAD) Epidemiología, Diagnostico, Control, Prevención y Tratamiento del Síndrome Metabólico en Adultos. 2010 [Internet] [Citado el 19 de noviembre de 2020] Disponible en: https://www.revistaalad.com/pdfs/100125-44.pdf

21.

Seo

Choe

Torabi

MR.

Is waist circumference ⩾ 102/88 cm better than body mass index ⩾ 30 to predict hypertension and diabetes development regardless of gender, age group, and race/ethnicity? Meta-analysis. Prev Med. 2017;97:100-108.

22.

Balkau

Deanfield

Haffner

. International Day for the Evaluation of Abdominal Obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in 168,000 primary care patients in 63 countries. Circulation. 2007;116(17):1942-1951. doi:10.1161/CIRCULATIONAHA.106.676379.

23.

Vásquez-Romero

Zuzunaga-Montoya

Loayza-Castro

Vigil-Ventura

Ramos

Vera-Ponce

VJ.

Prevalence of obesity according to body mass index, waist circumference, and waist-to-height ratio in Peru: a systematic review and meta-analysis. Obes Pillars. 2025;13:100166.

24.

Collazos-Huamán

LDC

Guerreros-Espino

Herrera-Añazco

Benites-Zapata

. Association between glycemic control and albuminuria among Peruvian adults with diabetes mellitus 2: a cross-sectional analytical study. Sao Paulo Med J. 2022;140(6):767-774.

25.

Kidney Disease: Improving Global Outcomes (KDIGO) CKD-MBD Work Group. KDIGO clinical practice guideline for the diagnosis, evaluation, prevention, and treatment of chronic kidney disease-mineral and bone disorder (CKD-MBD). Kidney Int. 2009;76(113):S1-S130.

26.

Ogbera

Azenabor

AO.

Hyperuricaemia and the metabolic syndrome in type 2 DM. Diabetol Metab Syndr. 2010;2(1):1-7.

27.

Dietary factors and risk of gout and hyperuricemia: a meta-analysis and systematic review. Asia Pac J Clin Nutr. 2018;27(6):1344-1356.

28.

Schlesinger

Neuenschwander

Schwedhelm

, et al. Food Groups and risk of overweight, obesity, and weight gain: a systematic review and dose-response meta-analysis of prospective studies. Adv Nutr. 2019;10(2):205-218.

29.

Choi

Atkinson

Karlson

Willett

Curhan

Alcohol intake and risk of incident gout in men: a prospective study. Lancet. 2004;363:1277-1281.

30.

Hannou

Haslam

McKeown

Herman

MA.

Fructose metabolism and metabolic disease. J Clin Investig. 2018;128(2):545-555.

31.

Kovalskys

Fisberg

Gómez

, et al. Energy intake and food sources of eight Latin American countries: results from the Latin American study of Nutrition and Health (ELANS). Public Health Nutr. 2018;21(14):2535-2547.

32.

Kim

Lee

Yoo

, et al. The relationship between the regional abdominal adipose tissue distribution and the serum uric acid levels in people with type 2 diabetes mellitus. Diabetol Metab Syndr. 2012;4(1):3.

33.

de Oliveira

Burini

RC.

High plasma uric acid concentration: causes and consequences. Diabetol Metab Syndr. 2012;4(1):12.

34.

Yadav

Kataria

Saini

Yadav

Role of leptin and adiponectin in insulin resistance. Clin Chim Acta. 2013;417:80-84.

35.

Hikita

Ohno

Mori

Ichida

Yokose

Hosoya

Relationship between hyperuricemia and body fat distribution. Intern Med. 2007;46(17):1353-1358.

36.

Bekele

Tadewose

Bizuayehu

Hyperuricemia and metabolic syndrome in type 2 diabetes mellitus patients at Hawassa university comprehensive specialized hospital, South West Ethiopia. BMC Endocr Disord. 2017;17(1):76-78.

37.

Koga

Saito

Mukai

Kasayama

Yamamoto

Factors contributing to increased serum urate in postmenopausal Japanese females. Climacteric. 2009;12(2):146-152.

38.

Halperin Kuhns

Woodward

OM.

Sex differences in urate handling. Int J Mol Sci. 2020;21(12):4269.

39.

Woodward

Köttgen

Coresh

Boerwinkle

Guggino

Köttgen

Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout. Proc Natl Acad Sci USA. 2009;106(25):10338-10342.

40.

Matsuo

Chiba

Nagamori

, et al. Mutations in glucose transporter 9 gene SLC2A9 cause renal hypouricemia. Am J Hum Genet. 2008;83(6):744-751.

41.

Bonakdaran

Kharaqani

Association of serum uric acid and metabolic syndrome in type 2 diabetes. Curr Diabetes Rev. 2014;10(2):113-117.

42.

Neupane

Dubey

Gautam

, et al. Association between serum uric acid, urinary albumin excretion, and glycated hemoglobin in type 2 diabetic patient. Niger Med J. 2016;57(2):119-123.

43.

Oliveira

Mintem

Oliveira

, et al. Uric acid is independent and inversely associated to glomerular filtration rate in young adult Brazilian individuals. Nutr Metab Cardiovasc Dis. 2020;30(8):1289-1298.

44.

Huang

Zhang

, et al. Hyperuricemia is associated with metabolic syndrome in the community very elderly in Chengdu. Sci Rep. 2020;10(1):1-7.

Association Between Abdominal Obesity and Hyperuricemia in Peruvian Adults with Type 2 Diabetes Mellitus

Abstract

Background:

Methods:

Results:

Conclusion:

Plain Language Summary

Keywords

Introduction

Methods

Study Design

Population, Sample, and Statistical Power

Variables and Measurements

Statistical Analysis

Ethical Aspects

Results

General Characteristics of the Population

Bivariate Analysis

Regression Analysis

Discussion

Conclusion

Footnotes

ORCID iDs

Ethical Considerations

Consent to Participate

Consent for Publication

Author Contributions

Funding

Conflicting Interests

Data Availability Statement

References