A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease.

INTRODUCTION/OBJECTIVE: The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. METHODS: The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. RESULTS: A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from -7.08 ml/min/1.73 m2/year to -4.27 ml/min/1.73 m2/year in high-risk patients (P = .0003), -2.65 to -1.04 in intermediate risk, and -3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P < .001). CONCLUSIONS: Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

Real World Evidence and Clinical Utility of KidneyIntelX on Patients With Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes.

INTRODUCTION AND OBJECTIVE: The lack of precision to identify patients with early-stage diabetic kidney disease (DKD) at near-term risk for progressive decline in kidney function results in poor disease management often leading to kidney failure requiring unplanned dialysis. The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to generate a risk score for progressive decline in kidney function over 5-year in adults with early-stage DKD. Our objective was to assess the impact of KidneyIntelX on management and outcomes in a Health System in the real-world evidence (RWE) study. METHODS: KidneyIntelX was introduced into a large metropolitan Health System via a population health-defined approved care pathway for patients with stages 1 to 3 DKD between [November 2020 to March 2022]. Decision impact on visit frequency, medication management, specialist referral, and selected lab values was assessed. We performed an interim analysis in patients through 6-months post-test date to evaluate the impact of risk level with clinical decision-making and outcomes. RESULTS: A total of 1686 patients were enrolled in the RWE study and underwent KidneyIntelX testing and subsequent care pathway management. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. The median baseline eGFR was 59 ml/minute/1.73 m2, urine albumin-creatinine ratio was 69 mg/g, and HbA1c was 7.7%. After testing, a clinical encounter in the first month occurred in 13%, 43%, and 53% of low-risk, intermediate-risk, and high-risk patients, respectively and 46%, 61%, and 71% had at least 1 action taken within the first 6 months. High-risk patients were more likely to be placed on SGLT2 inhibitors (OR = 4.56; 95% CI 3.00-6.91 vs low-risk), and more likely to be referred to a specialist such as a nephrologist, endocrinologist, or dietician (OR = 2.49; 95% CI 1.53-4.01) compared to low-risk patients. CONCLUSIONS: The combination of KidneyIntelX, clinical guidelines and educational support resulted in changes in clinical management by clinicians. After testing, there was an increase in visit frequency, referrals for disease management, and introduction to guideline-recommended medications. These differed by risk category, indicating an impact of KidneyIntelX risk stratification on clinical care.

Plasma and urine biomarkers in chronic kidney disease: closer to clinical application.

PURPOSE OF REVIEW: Chronic kidney disease (CKD) is a silent disease, causing significant health and economic burden worldwide. It is of strong clinical value to identify novel prognostic, predictive, and pharmacodynamic biomarkers of kidney function, as current available measures have limitations. We reviewed the advances in biomarkers in CKD over the preceding year. RECENT FINDINGS: The most frequently studied prognostic plasma biomarkers during recent year were plasma TNFR1, TNFR2, KIM1 and urinary MCP-1 and EGF. New biomarkers such as plasma WFDC2, MMP-7, EFNA4, EPHA2 may also have potential to serve as prognostic biomarkers. There is a shortage of data on biomarkers that are predictive of response to treatments. Data on novel biomarkers to serve as pharmacodynamic biomarkers are limited, but there are emerging data that plasmaTNFR1, TNFR2, KIM-1 are not only prognostic at baseline, but can also contribute to time-updated response signals in response to therapy. SUMMARY: Data continue to emerge on applicable biomarkers for prognostic clinical risk stratification, prediction of therapeutic response and assessment of early efficacy of interventions. Although more studies are needed for refinement and specific clinical utility, there seems to be sufficient data to support clinical implementation for some biomarkers.

Cardiometabolic Risk Factor Changes Observed in Diabetes Prevention Programs in US Settings: A Systematic Review and Meta-analysis.

BACKGROUND: The Diabetes Prevention Program (DPP) study showed that weight loss in high-risk adults lowered diabetes incidence and cardiovascular disease risk. No prior analyses have aggregated weight and cardiometabolic risk factor changes observed in studies implementing DPP interventions in nonresearch settings in the United States. METHODS AND FINDINGS: In this systematic review and meta-analysis, we pooled data from studies in the United States implementing DPP lifestyle modification programs (focused on modest [5%-7%] weight loss through ≥150 min of moderate physical activity per week and restriction of fat intake) in clinical, community, and online settings. We reported aggregated pre- and post-intervention weight and cardiometabolic risk factor changes (fasting blood glucose [FBG], glycosylated hemoglobin [HbA1c], systolic or diastolic blood pressure [SBP/DBP], total [TC] or HDL-cholesterol). We searched the MEDLINE, EMBASE, Cochrane Library, and Clinicaltrials.gov databases from January 1, 2003, to May 1, 2016. Two reviewers independently evaluated article eligibility and extracted data on study designs, populations enrolled, intervention program characteristics (duration, number of core and maintenance sessions), and outcomes. We used a random effects model to calculate summary estimates for each outcome and associated 95% confidence intervals (CI). To examine sources of heterogeneity, results were stratified according to the presence of maintenance sessions, risk level of participants (prediabetes or other), and intervention delivery personnel (lay or professional). Forty-four studies that enrolled 8,995 participants met eligibility criteria. Participants had an average age of 50.8 years and body mass index (BMI) of 34.8 kg/m2, and 25.2% were male. On average, study follow-up was 9.3 mo (median 12.0) with a range of 1.5 to 36 months; programs offered a mean of 12.6 sessions, with mean participant attendance of 11.0 core sessions. Sixty percent of programs offered some form of post-core maintenance (either email or in person). Mean absolute changes observed were: weight -3.77 kg (95% CI: -4.55; -2.99), HbA1c -0.21% (-0.29; -0.13), FBG -2.40 mg/dL (-3.59; -1.21), SBP -4.29 mmHg (-5.73, -2.84), DBP -2.56 mmHg (-3.40, 1.71), HDL +0.85 mg/dL (-0.10, 1.60), and TC -5.34 mg/dL (-9.72, -0.97). Programs with a maintenance component achieved greater reductions in weight (additional -1.66kg) and FBG (additional -3.14 mg/dl). Findings are subject to incomplete reporting and heterogeneity of studies included, and confounding because most included studies used pre-post study designs. CONCLUSIONS: DPP lifestyle modification programs achieved clinically meaningful weight and cardiometabolic health improvements. Together, these data suggest that additional value is gained from these programs, reinforcing that they are likely very cost-effective.

Evaluation of Short-Term Changes in Serum Creatinine Level as a Meaningful End Point in Randomized Clinical Trials.

Observational studies have shown that acute change in kidney function (specifically, AKI) is a strong risk factor for poor outcomes. Thus, the outcome of acute change in serum creatinine level, regardless of underlying biology or etiology, is frequently used in clinical trials as both efficacy and safety end points. We performed a meta-analysis of clinical trials to quantify the relationship between positive or negative short-term effects of interventions on change in serum creatinine level and more meaningful clinical outcomes. After a thorough literature search, we included 14 randomized trials of interventions that altered risk for an acute increase in serum creatinine level and had reported between-group differences in CKD and/or mortality rate ≥3 months after randomization. Seven trials assessed interventions that, compared with placebo, increased risk of acute elevation in serum creatinine level (pooled relative risk, 1.52; 95% confidence interval, 1.22 to 1.89), and seven trials assessed interventions that, compared with placebo, reduced risk of acute elevation in serum creatinine level (pooled relative risk, 0.57; 95% confidence interval, 0.44 to 0.74). However, pooled risks for CKD and mortality associated with interventions did not differ from those with placebo in either group. In conclusion, several interventions that affect risk of acute, mild to moderate, often temporary elevation in serum creatinine level in placebo-controlled randomized trials showed no appreciable effect on CKD or mortality months later, raising questions about the value of using small to moderate changes in serum creatinine level as end points in clinical trials.

Genetic studies of type 2 diabetes in South Asians: a systematic overview.

Diabetes Mellitus, which affects 366 million people worldwide, is a leading cause of mortality, morbidity, and loss of quality of life. South Asians, comprising 24% of the world's population, suffer a large burden of type 2 diabetes. With intriguing risk phenotypes, unique environmental triggers, and potential genetic predisposition, South Asians offer a valuable resource for investigating the pathophysiology of type 2 diabetes. Genomics has proven its potential to underpin some of the etiology of type 2 diabetes by identifying a number of susceptibility genes, but such data are scarce and unclear in South Asians. We present a systematic review of studies on the genetic basis of type 2 diabetes or its complications in South Asians published between 1987-2012, and discuss the findings and limitations of the available data. Of the 91 eligible studies meeting our inclusion criteria, a vast majority included Indian populations, followed by a few in those of Pakistani origin, while other South Asian countries were generally under-represented. Though a large number of studies focused on the replication of findings from genome-wide association studies (GWAS) in European populations, a few studies explored new genes and pathways along with GWAS in South Asians and suggested the potential to unravel population- specific susceptibility genes in this population. We find encouraging improvements in study designs, sample sizes and the numbers of genetic variants investigated over the last five years, which reflect the existing capacity and scope for large-scale genetic studies in South Asians.

Global rural diabetes prevalence: a systematic review and meta-analysis covering 1990-2012.

AIM: To investigate diabetes prevalence in rural areas globally and how it has changed over time in high-income countries (HIC) and low-middle income countries (LMIC). METHODS: We systematically searched four electronic databases (MEDLINE, EMBASE, Cochrane Library, and CINAHL) for studies reporting rural prevalence of diabetes between January 1990 and January 2012. We used random effect meta-analysis to estimate pooled prevalence and metaregression to identify factors that may be associated with our estimates. FINDINGS: We included 109 population-based surveys involving 1,100,746 individuals. Pooled global rural prevalence of diabetes was 6.8% (95% confidence intervals: 6.1-7.6) with no gender difference. Considering five-year increments between 1985 and 2011, when studies were conducted, global rural prevalence was 5.7% (3.5-7.9) during 1985-1989 and 8.7% (6.8-10.7) during 2005-2011. In LMICs, rural diabetes prevalence was 1.8% (1.0-2.6) during 1985-1989 and 7.5% (5.6-9.5) during 2005-2011. In HICs, rural diabetes prevalence was 8.2% (6.0-10.4) during 1985-1989 and 14.3% (8.7-20) in the most recent period. CONCLUSION: Diabetes prevalence has increased in all rural areas globally but relative growth was faster in LMIC than HIC rural areas. These data suggest a need to expand diabetes surveillance to rural areas using standardized measures, as well as the need to devise and deploy appropriate prevention and control interventions.

Diabetes in the Middle East and North Africa.

AIMS: Even though the Middle East and North Africa (MENA) region had the highest comparative prevalence of diabetes in 2012, little is known about the nuances of diabetes risk and capacity to address the burdens. To provide a comprehensive overview, we reviewed the literature on diabetes in the MENA region. METHODS: We conducted a systematic search in PubMed between January 1990 and January 2012 for studies on diabetes in the MENA region without language restriction. RESULTS: There was a paucity of country-specific epidemiology data in the region. Diabetes prevalence varied widely across studies, from 2.5% in 1982 to 31.6% in 2011. Older age and higher body mass index were the most strongly associated risk factors for diabetes. Among people with diabetes, over half did not meet recommended care targets. In addition, macrovascular and microvascular complications were observed in 9-12% and 15-54% of diabetes population, respectively. CONCLUSIONS: This review suggests a need for more representative surveillance data in this noteworthy focal point of the global diabetes epidemic. Such actions will not only help to understand the actual burden of diabetes but also motivate actions on design and implementation of diabetes prevention and control programs.

Rural diabetes prevalence quintuples over twenty-five years in low- and middle-income countries: a systematic review and meta-analysis.

AIMS: To verify the assertions that diabetes pandemic may be spreading across rural parts of low- and middle-income countries (LMICs), we performed a systematic review of published studies reporting diabetes prevalence in rural parts of LMICs. METHODS: Electronic databases (EMBASE and MEDLINE) were searched for papers published from 1990 to 2011. Two independent reviewers screened the articles using structured criteria for inclusion and performed full-text reviews. Pooled prevalence of diabetes was estimated using meta-analysis. Potential factors influencing the estimates were identified by meta-regression and used for sensitivity analyses. RESULTS: Rural prevalence of diabetes of LMICs was 5.6% (95% CI=4.6-6.6), and similar between men and women. This estimate remained robust in separate sensitivity analyses accounting for study quality, level of heterogeneity, age, and sex. In a multivariate meta-regression analysis, pooled prevalence varied by study year and region. Diabetes prevalence increased over time, from 1.8% (1.0-2.6) in 1985-1989, 5.0% (3.8-6.3) in 1990-1994, 5.2% (4.1-6.2) in 1995-1999, 6.4% (5.1-7.7) in 2000-2004, and to 8.6% (6.4-10.7) for 2005-2010 (p=0.001 for secular trend). CONCLUSIONS: Prevalence of diabetes in rural parts of LMICs has risen dramatically. As 55% of LMIC populations live in rural areas, this trend has enormous implications for the global burden of diabetes.

San Antonio heart study diabetes prediction model applicable to a Middle Eastern population? Tehran glucose and lipid study.

OBJECTIVES: To assess the validity of the San Antonio heart study (SAHS) diabetes prediction model in a large representative Iranian population. METHODS: A risk function derived from data in the SAHS to predict the 7.5-year risk of diabetes, was tested for its ability to predict incident diabetes in 3,242 individuals aged >or=20 years. The performance or ability to accurately predict diabetes risk, of the SAHS function compared with the performance of risk functions developed specifically from the Tehran lipid and glucose study. Comparisons included goodness of fit, discrimination, and calibration. RESULTS: The participants were followed for 6.3 years. The area under the receiver operating characteristic curve (AROC) for diabetes of SAHS model was 0.83 (95% CI 0.80-0.86). The model overestimated the risk of diabetes in TLGS population with the overall bias of 111%. After the recalibration, the model-predicted probability agreed well with the actual observed 6-year risk of diabetes. DISCUSSION AND CONCLUSION: The American SAHS was a prediction model for diabetes with good discrimination in an Iranian target population after calibration.

Weight change and incident metabolic syndrome in Iranian men and women; a 3 year follow-up study.

BACKGROUND: Although the association of weight gain and developing metabolic syndrome (MetS) has been reported in the Western and Asian populations, data on the gender-stratified effects of weight change (including weight loss) on incident MetS and its components in the Middle East Caucasians is still scarce. METHODS: A total of 1431 men and 2036 women aged > OR years with BMI > 18.5 kg/m2 were followed over 3 years. Multivariate logistic regression analysis was used to estimate the relative risk (RR) of MetS and its components (the Adult Treatment Panel III definition) associated with gender-stratified quintiles of percent weight change. Subjects with MetS at baseline were excluded for analyzing the RR of MetS. RESULTS: There was 20.4% (95% CI, 19.6-21.2) age-adjusted incident MetS (18.4% male vs. 23.1% women). In men, mild weight gain (WG) predicted high waist circumference (WC) and high triglyceride; moderate WG predicted MetS (RR 2.5, 95% CI 1.4-4.3), high WC and high blood pressure (BP); large WG predicted MetS (RR 3.2, 95% CI 1.8-5.7) and its components, except for high fasting plasma glucose. In women, mild WG predicted MetS (RR 2.5, 95% CI 1.4-4.3), high WC and high BP; moderate WG predicted Mets (RR 4.6, 95% CI 2.7-8.0), high WC and high triglyceride; large WG predicted MetS (RR 6.6, 95% CI 3.8-11.3) and its components except for low HDL-cholesterol. Mild weight loss had protective effect on high WC in both genders and MetS in men (RR 0.5, 95% CI 0.26-0.97, P = 0.04). CONCLUSION: Weight change showed different effects on MetS in men and women. In women, mild WG predicted MetS; however, mild weight loss was protective against MetS in men and high WC in both genders.

Prevalence of metabolic syndrome by the Adult Treatment Panel III, International Diabetes Federation, and World Health Organization definitions and their association with coronary heart disease in an elderly Iranian population.

INTRODUCTION: To determine the prevalence of the metabolic syndrome (MS) in an Iranian elderly population and show its association with coronary heart disease (CHD). MATERIALS AND METHODS: This is a cross-sectional study on 720 Iranian men and women aged > or = 65 years who participated in the Tehran Lipid and Glucose Study (TLGS). Logistic regression analysis was used to estimate the odds ratio (OR) of developing CHD in model 1, an age-adjusted model; model 2, adjusted for age, smoking status, premature history of CHD and low-density lipoprotein (LDL) cholesterol; and model 3, adjusted for mentioned variables plus the MS components. RESULTS: The prevalence of MS was 50.8%, 41.8% and 41.9% based on the Adult Treatment Panel (ATPIII), the World Health Organisation (WHO), and the International Diabetes Federation (IDF) definitions, respectively. The IDF definition showed high agreement with the ATPIII definition. Age-adjusted OR (95% CI) of the MS for CHD was 1.6 (1.2 to 2.2) by both the ATPIII and WHO definitions and 1.4 (1.0 to 1.9) by the IDF definition. IDF-defined MS lost its association with CHD in model 2. In model 3, obesity (WHO definition) and high blood pressure (ATPIII and WHO definitions) were associated with CHD. CONCLUSIONS: In an elderly Iranian population MS is highly prevalent. ATPIII and WHO definitions seem to be more pertinent than IDF for screening CHD risk. None of these definitions showed association with CHD when considering their components.

Metabolic syndrome in normal-weight Iranian adults.

BACKGROUND: This study provides the first reported estimates of the prevalence of the metabolic syndrome in a normal-weight Iranian population. SUBJECTS AND METHODS: In this population-based cross-sectional study, the study population consisted of a representative sample of 1737 males and 1707 females aged > or = 20 years with normal body mass index (BMI) (18.5-24.9 kg/m2 for both genders). The metabolic syndrome was defined according to the Adult Treatment Panel III guidelines. We present means and proportions, and multivariate odds ratios that quantify the association between metabolic syndrome and normal BMI quartiles, controlling for age, physical activity, smoking and education. RESULTS: The overall prevalence of the metabolic syndrome in normal-weight men and women were 9.9% and 11.0% (P=0.2) respectively. Men had a lower BMI than women, while their waist circumference (WC) was higher. The prevalence of high WC and low high-density lipoprotein cholesterol (HDL-C) was higher in women, while high blood pressure, high triglyceride levels and having at least one metabolic risk factor were more prevalent in men. Individuals in the highest category of normal BMI had significantly higher odds for being at risk for metabolic syndrome compared to those in the first category (OR: 5.21 for men and 2.15 for women). There was an increasing trend in odds for having all the metabolic syndrome components except for high fasting blood sugar (FBS) and high WC in men. Women showed a similar increasing trend except for high FBS across normal BMI quartiles. CONCLUSION: The prevalence of the metabolic syndrome in normal-weight Iranian adults is relatively high. Therefore, interventions for prevention of cardiovascular disease could be considered in this population.

Prevalence of metabolic syndrome in Iranian adult population, concordance between the IDF with the ATPIII and the WHO definitions.

BACKGROUND: Different criteria have been proposed by the WHO, the ATPIII and the International Diabetes Federation (IDF) for the diagnosis of the metabolic syndrome (MES). The objective of this study was to estimate the prevalence of this syndrome using the IDF definition among Iranian adults and to compare it with the prevalence estimated using the two other definitions. MATERIALS AND METHODS: The prevalence of the MES was determined according to the three different proposals in 10,368 men and women aged >/=20 years participated in the cross-sectional phase of the Tehran Lipid and Glucose Study. To assess the degree of agreement between different MES definitions, the k test was used. RESULTS: The prevalence of MES (95% confidence interval) was 32.1% (31.2-33.0) by the IDF definition, 33.2% (32.3-34.1) by the ATPIII and 18.4% (17.6-19.2) according to the WHO definition. The sensitivity, specificity of the IDF definition for detecting MES were 91%, 89% for the ATPIII and 73%, 77% for the WHO definition, respectively. The k statistics for the agreement of the IDF definition was 66.3+/-0.01 with the ATPIII and 39.5+/-0.01 with the WHO definition. CONCLUSION: In the Iranian population, the IDF definition for MES has a good concordance with the ATP III definition and a low concordance with the WHO definition.

The prospective association of general and central obesity variables with incident type 2 diabetes in adults, Tehran lipid and glucose study.

OBJECTIVE: To investigate which anthropometric index is the best predictor of diabetes in relation to age. METHODS: In this longitudinal study 4479 non-diabetic men and women aged > or =20 years were followed for 3.6 years. Diabetes with its risk factors and obesity were defined according to the ADA and the WHO criteria, respectively. Logistic regression analysis was used to estimate the odds ratio (OR) of developing diabetes in model 1 including only the anthropometric measure and in model 2 adjusted for common diabetes risk factors and in model 3 adjusted for other anthropometric indices plus all the variables in model 2. RESULTS: A total of 166 new cases of type 2 diabetes were diagnosed. In subjects aged <60 years general obesity and high waist-to-hip ratio (WHR) predicted diabetes in all three models with OR of 2.4 and 2.6 in model 3, respectively, while high waist circumference (WC) lost it association with diabetes in the full model. In subjects aged > or =60 years, however, high WC was the only independent predictor of diabetes in model 3 with OR of 3.8 while high WHR and general obesity predicted diabetes in models 1 and 2, respectively. CONCLUSION: General obesity and high WHR in Iranian subjects aged <60 years and high WC in older ones are the important predictors of type 2 diabetes.