Effectiveness of telehealth nutritional therapy in preventing chronic kidney disease among adults with type 2 diabetes and obesity: a real-world, retrospective, propensity score–matched cohort study

Effectiveness of telehealth nutritional therapy in preventing chronic kidney disease among adults with type 2 diabetes and obesity: a real-world, retrospective, propensity score–matched cohort study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Effectiveness of telehealth nutritional therapy in preventing chronic kidney disease among adults with type 2 diabetes and obesity: a real-world, retrospective, propensity score–matched cohort study View ORCID Profile Shaminie J Athinarayanan , Petter Bjornstad , Priya V Shanmugam , Thomas Weimbs , Adam Wolfberg , Jeff S Volek , Jonathan Himmelfarb , Richard J Johnson doi: https://doi.org/10.1101/2025.10.17.25338238 Shaminie J Athinarayanan 1 Research Department, Virta Health , Denver, CO PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shaminie J Athinarayanan For correspondence: shaminie{at}virtahealth.com Petter Bjornstad 2 Kidney Research Institute and Division of Nephrology, Department of Medicine, University of Washington , Seattle, WA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Priya V Shanmugam 1 Research Department, Virta Health , Denver, CO PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas Weimbs 3 Department of Molecular, Cellular, and Developmental Biology, University of California , Santa Barbara, CA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adam Wolfberg 1 Research Department, Virta Health , Denver, CO MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeff S Volek 4 Department of Human Sciences, College of Education and Human Ecology, The Ohio State University , Columbus, OH, USA PhD RD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jonathan Himmelfarb 5 Department of Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard J Johnson 6 Division of Renal Diseases and Hypertension, Department of Medicine, University of Colorado Anschutz Medical Campus , Aurora, CO, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Objective To evaluate the real-world effectiveness and safety of a telehealth-delivered, individualized nutrition therapy (VINT) for the prevention and progression of chronic kidney disease (CKD) among adults with type 2 diabetes and obesity. Design Retrospective, propensity score–matched cohort study using administrative claims data. Setting Komodo Healthcare Map™, a longitudinal U.S. claims database containing medical, and pharmacy data Participants 8,391 adults enrolled in the VINT program were matched 1:1 with 8,391 usual care controls on demographic, clinical, and medication covariates, with up to five years of follow-up. Main outcome measures Primary outcomes were new-onset CKD, CKD stage ≥3, and CKD stage ≥4. Secondary outcomes included regression or stability among those with baseline CKD and safety outcomes (kidney stones, gout, and acidosis). Cox proportional hazards and Poisson regression models were used to estimate hazard ratios (HRs) and incidence rate ratios (IRRs). Results VINT participation was associated with lower incidence of new-onset CKD (10.8 vs 15.7 per 1,000 person-years; HR 0.67, 95% CI 0.54–0.83; p <0.001), CKD stage ≥3 (HR 0.55, 95% CI 0.42–0.72; p <0.001), and CKD stage ≥4 (HR 0.34, 95% CI 0.16–0.71; p =0.004). Among participants with baseline CKD, regression or stability occurred in 96.8% of VINT versus 87.6% of controls ( p =0.005). There was no increased risk of kidney stones, acidosis, or gout. Conclusions In this large, real-world matched cohort, participation in a telehealth-delivered individualized lifestyle intervention emphasizing carbohydrate restriction was associated with significantly lower CKD incidence and progression, without increased adverse renal outcomes. What is already known on this topic Adults with type 2 diabetes and obesity are at high risk of chronic kidney disease (CKD) and kidney-related complications. Current drug therapies, while beneficial, leave substantial residual risk and are often limited by cost, access, and adherence. Scalable lifestyle-based approaches that can be delivered remotely are urgently needed to prevent CKD onset and progression. What this study adds In a large real-world matched cohort, a telehealth-delivered individualized nutrition therapy program was linked to a 33% lower risk of new-onset CKD and up to a 66% lower risk of advanced CKD (stage 4 and beyond). Among participants with existing CKD, disease regression or stability occurred in nearly 97%, compared with 88% of controls. The intervention was safe, with no increased risk of kidney stones, acidosis, or gout. Introduction The prevalence of chronic kidney disease (CKD) has risen sharply, particularly among individuals with type 2 diabetes (T2D)( 1 , 2 ). CKD carries profound consequences, including reduced quality of life, elevated cardiovascular risk, and increased morbidity and mortality ( 2 – 4 ). Although recent therapeutic advances including sodium-glucose cotransporter 2 inhibitors (SGLT2i)( 5 , 6 ), nonsteroidal mineralocorticoid receptor antagonists ( 7 ), and GLP1 receptor agonists (GLP1-RA) ( 8 , 9 ) offer benefit, these agents are costly, may cause adverse effects, and leave substantial residual risk of CKD progression ( 1 , 2 , 10 ). Accordingly, lifestyle interventions, particularly dietary strategies, remain critical either alone or in combination with pharmacotherapy. High-protein diets accelerate CKD progression in animals ( 11 , 12 ), prompting investigators to study protein restriction as an intervention ( 13 ). However, benefits were modest or limited to subgroups, and extreme restriction was found to have deleterious effects on overall morbidity and mortality ( 13 ). Current guidelines therefore recommend avoiding high protein intake but emphasize only modest restriction (0.6–1.0 g/kg/day) for individuals with moderate CKD ( 14 ). Similarly, caloric restriction may confer some benefit in CKD linked to metabolic syndrome or diabetes ( 15 ). Evidence, however, implicates high carbohydrate intake in CKD progression. Carbohydrate excess contributes to metabolic syndrome and diabetes, and experimental studies demonstrate that diets high in sugar or fructose can accelerate CKD ( 16 , 17 ). We studied a remotely administered, digitally supported nutrition and lifestyle program developed primarily for individuals with type-2 diabetes and obesity/overweight, that focuses on carbohydrate restriction leading to varying degrees of nutritional ketosis ( 18 , 19 ). Adherence to this program leads to a high rate of remission of type-2 diabetes ( 18 , 19 ). Post-hoc analyses of clinical trial data from this intervention also demonstrated a reversal of eGFR slope decline, with a significant increase in eGFR from baseline to two years, corresponding to a slope of +0.91 mL/min/1.73m 2 /year ( 20 ). The multimodal intervention, integrating personalized carbohydrate restriction with continuous remote care, individualized treatment, and real-time clinical support, likely achieved these effects by addressing multiple interrelated mechanisms rather than isolated pathways. Beyond eGFR benefits, both clinical trials and real-world studies show improvements in glycemia ( 18 , 19 , 21 ), weight ( 18 , 19 , 21 ), and blood pressure ( 22 ). Kidney-specific benefits include durable reductions in urinary albumin-to-creatinine ratio (UACR) among patients with stage 2–3 albuminuria, sustained for up to two years ( 23 ). In this claims-based analysis, we evaluated the impact of this multimodal, carbohydrate-restricted intervention and lifestyle program on the development and progression of CKD compared with matched controls. Outcomes included incidence of new CKD diagnoses, the occurrence of advanced CKD stages during follow-up, and stability or regression of existing CKD. Safety outcomes included kidney stone, gout, and acidosis including diabetes ketoacidosis (DKA) diagnoses. Finally, we explored whether metabolic response, defined by ketone levels, weight loss and A1c change was associated with CKD risk within the intervention cohort. Materials and Methods Data Source The Komodo Healthcare Map™ is a longitudinal claims database that captures medical, laboratory, and pharmaceutical claims from approximately 330 million individuals across U.S Medicare, Medicaid, and commercial payers ( 24 ). For this study, demographic information for the participants enrolled in the program, along with aggregated enrollment and follow-up variables, were linked to the Komodo claims database. To construct a control cohort, claims data from 2.39 million patients were drawn from the Komodo Healthcare Map™. The data iteration dated August 25, 2025, was used for the present analysis. All analyses were conducted within the Sentinel database, a real-world data analytics environment provided by Komodo Health. Study Population The study cohort comprised adults who enrolled in the Virta Individualized Nutrition Therapy (VINT) program with T2D, overweight, or obesity. Comprehensive details on eligibility, nutritional intervention, and medication management are provided in the Supplementary Methods; a brief description is given below. Eligibility Adults aged ≥18 years with overweight, obesity, T2D, or prediabetes were eligible for enrollment in the VINT diabetes reversal or sustainable weight loss programs. Individuals with conditions considered unsafe for nutritional ketosis (e.g., type 1 diabetes, or pregnancy) were excluded. Comprehensive inclusion and exclusion criteria are provided in the Supplementary method. Intervention VINT delivered continuous remote care via a telemedicine platform, integrating individualized nutrition therapy, protocol-guided medication management, and ongoing health coaching. The intervention emphasized individualized carbohydrate restriction with moderate protein intake to achieve and maintain nutritional ketosis [target β-hydroxybutyrate (BHB) 0.5–3.0 mmol/L]. Protein intake was maintained at 1.2–1.5 g/kg reference body weight/day, and fat served as the primary energy source, adjusted to satiety and energy needs. The dietary approach prioritized whole, minimally processed foods. Participants received continuous remote support from health coaches and medical providers and education in nutrition, behavior change, and adaptation strategies. Biomarkers including blood glucose, BHB, and body weight were monitored and reviewed to provide individualized feedback, optimize adherence, and ensure safety. Biomarker monitoring was performed daily at program initiation, particularly during the first three months, and was subsequently individualized according to each participant’s preferences and tolerance of the nutritional intervention. Nutritional recommendations were iteratively tailored to each participant’s weight loss, glycemic control, cardiometabolic profile, and maintenance of nutritional ketosis. Medication Management Licensed providers supervise medication adjustments to ensure safety, of concern when patients on insulin and other diabetes-related medications rapidly reduce their carbohydrate intake. Hypoglycemia-prone therapies (e.g., insulin, sulfonylureas) were typically reduced at initiation, while antihypertensives, diuretics, and other cardiometabolic agents were titrated based on clinical response. Medication management was adaptive and continuous, with deprescription or reintroduction guided by daily biomarker tracking and real-time provider feedback. Claims Analysis Inclusion Criteria Eligible patients were those who had enrolled in either the diabetes reversal or sustainable weight loss programs between August 2015 and September 2024 and maintained continuous participation in VINT for at least six months following the enrollment date. To construct the control cohort, patients were assigned a randomly selected index date aligned with the distribution of enrollment dates in the treated cohort. The same inclusion criteria were applied to both the treated and control cohorts for the claims analysis, requiring continuous claims data (defined as no gap greater than 30 days) for at least one year prior to the index or enrollment date and at least six months after, without early censoring. Baseline demographics, comorbidities and medications Baseline characteristics included demographic variables (age, sex, and race/ethnicity), clinical comorbidities, medication use, and healthcare costs (see Supplementary method). All diagnoses were identified at least once using tenth Revision, Clinical Modification (ICD-10-CM) codes from medical claims, regardless of diagnosis position (primary or secondary; Supplementary Table S1). Detail drug names used to identify baseline and followup medications are listed in Supplementary Table S2. Study Outcomes The primary study outcomes were the time to: ( 1 ) the first occurrence of a new CKD diagnosis, regardless of stage or albuminuria; ( 2 ) the first occurrence of CKD stage 3 or higher (including stages 3, 3a, 3b, 4, 5, and ESKD, or initiation of renal dialysis if the diagnosis code were missing); and ( 3 ) the first occurrence of CKD stage 4 or higher (including stages 4, 5, and ESKD or initiation of renal dialysis if the diagnosis code was missing) during follow-up. Patients were censored at the time of loss to follow-up, defined as loss of continuous claims data coverage, or at study end if they remained event-free, with a maximum follow-up of 5 years. To assess the primary outcomes, individuals with any baseline CKD or albuminuria diagnosis were excluded from the matched data sets. The secondary outcomes were the occurrence of kidney stones, acidosis including diabetic ketoacidosis, and gout during follow-up. Exploratory outcomes included: ( 1 ) assessment of predictors associated with new-onset CKD diagnosis in the treated cohort, and ( 2 ) evaluation of CKD stage transitions (regression and stability, or progression) among those with baseline CKD and a corresponding follow-up diagnosis before censoring, to characterize CKD stage trajectories in both the VINT and Control groups. All the study endpoints used to assess all the outcomes were identified from administrative claims using ICD-10-CM diagnosis codes in any position (primary or secondary) and both ICD-10-PCS and HCPCS procedure codes. Detailed code lists are provided in Supplementary Table S1. Statistical analysis A full description of the statistical methods, including propensity score matching specifications, sensitivity analyses, and exploratory models, is provided in the Supplementary Methods. The study design, conduct, and reporting adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies ( 25 ). A completed STROBE checklist was prepared in accordance with the STROBE statement to ensure comprehensive and transparent reporting and is provided in the supplementary materials. Categorical variables were summarized using counts and percentages, while continuous variables were summarized using means and standard deviations. The program participants were matched 1:1 to controls using propensity scores derived from demographic, clinical, medication, baseline comorbidities and health care utilization variables. Baseline characteristics (1 year pre-index or enrollment), including any diagnosis of CKD, CKD stage, and presence of albuminuria, were prioritized as exact matches to achieve baseline balance. Details of the matching algorithm and covariates are provided in the Supplementary Methods. Two additional sensitivity analyses were performed. The first approach used 1:1 propensity score matching with exact matching on race and ethnicity and additional area-level covariates (“Tier 3” matching). This strategy slightly reduced the matched sample and limited power to assess regression and stability among participants with baseline CKD; it also reduced sensitivity for detecting incident stage 4+ CKD. The second approach applied inverse probability weighting and retained all eligible treated participants and controls. Further details are provided in the Supplementary Methods and Results. For all the primary outcomes, cox proportional hazards regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). All models were adjusted for age, sex, and race/ethnicity. Two additional models further adjusted for baseline diagnoses of type 2 diabetes and obesity, as well as follow-up medication use particularly those with kidney indication or kidney benefits including SGLT2i, incretin mimetics, ACEi/ARBs, nonsteroidal mineralocorticoid receptor antagonists, mineralocorticoid receptor antagonists, and diuretics, modeled both as categorical ever/never exposures and as continuous proportion of days covered (PDC). Incidence rates were expressed per 1,000 person-years. To address concerns related to kidney safety, we assessed whether participation in the VINT program was associated with an increased frequency of adverse events compared with matched controls. Safety outcomes included kidney stone, acidosis, diabetic ketoacidosis (DKA) and gout diagnoses, identified using diagnostic codes. Event rates were calculated per 1,000 person-years, and Poisson regression models with log(person-time) as the offset were used to estimate incidence rate ratios (IRRs) with 95% CIs. Prevalence of kidney stones and the occurrence of multiple events were also summarized. For DKA, cox proportional hazards regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Within the VINT cohort, exploratory analyses evaluated categorical predictors of CKD outcomes. Mean BHB levels (≥0.3 mmol/L vs. <0.3 mmol/L) over 6 months (all logged BHB values), weight change at 6 months (≥10% vs. <10%) and A1c change at 6 months or 1 year (≥0.5% vs. <0.5%) were assessed using Cox regression models stratified by ketone category, percentage weight change and A1c change category. In addition, generalized linear models (GLMs) with a log link and binomial distribution were used to estimate relative risks (RRs). Further analyses examined regression and stability of CKD stage defined as improvement or no worsening during follow-up in a subset of participants with baseline CKD and longitudinal follow-up claims data containing a new date and associated CKD ICD-10 code. These outcomes were analyzed using Chi-square tests and logistic regression. Finally, medication use among participants with either baseline CKD or follow-up CKD was described across therapeutic classes with kidney indication or potential kidney benefits (e.g., RAAS inhibitors, MRAs, finerenone, diuretics), with proportions and duration of use compared between groups. Statistical analyses were two-sided with an α level of 0.05 and 95% confidence intervals (CIs). All analyses were conducted using R software, with the R packages specified in the Supplementary Statistical Methods section. Results Study Participants Flow There were 78,755 treated participants and 2.39 million control participants included in the Komodo Sentinel working environment for this claims data analysis ( Figure 1 ). Of these, 8,633 treated participants and 218,887 control participants met the study inclusion criteria outlined in the Methods. After excluding individuals with other causes of kidney disease or related complications (Supplementary Table S1), 8,467 treated participants and 213,138 control participants remained in the dataset for propensity score matching. Download figure Open in new tab Figure 1. Patient attrition flowchart from the initial population in the Komodo Healthcare Sentinel working environment to the final analytic sample after 1:1 matching. Baseline Characteristics Following 1:1 matching, the final sample included 8,391VINT participants and 8,391 control (referred as usual care, UC) participants ( Figure 1 ; Table 1 ), representing approximately 10% of the original dataset. To evaluate representativeness, the matched treated cohort was compared with all VINT participants who were excluded for not meeting inclusion criteria. Baseline demographics and aggregated variables were generally consistent between the two groups, as assessed by standardized mean differences (SMDs). Most SMDs were <0.1 or close to 0.1, with the exception of race and ethnicity (Supplementary Table S3). After matching, no significant differences remained in baseline characteristics between the program and control groups for the covariates used in the matching as assessed by SMD (where all of them were <0.1), as well as for most other baseline comorbidities, conditions, and medications not included in the matching assessed mainly using SMDs. Both cohorts had a mean age of 52 years and an approximately equal distribution of males and females. Approximately 4% of participants had a baseline CKD diagnosis, and 2% had albuminuria. The mean follow-up times were 1.9 years and 1.8 years, respectively. Control index dates were matched to VINT’s enrollment date distribution to control for seasonal and calendar-time trends (Supplementary Figure 1). View this table: View inline View popup Table 1. Baseline characteristics of unmatched and propensity score matched cohorts of VINT and UC Primary Outcomes During follow-up, the incidence rate of new onset CKD was 10.8 per 1,000 person-years in the VINT group compared with 15.7 per 1,000 person-years in UC. For CKD stage 3 or higher, incidence rates were 6.2 vs. 11.1 per 1,000 person-years, and for CKD stage 4 or higher, 0.7 vs. 2.1 per 1,000 person-years, respectively ( Figure 2 ). Download figure Open in new tab Figure 2. Comparison of CKD Incidence Rates Between VINT and UC Groups In Cox proportional hazards models adjusted for age, sex, and race/ethnicity, participants in the program had a significantly lower risk of developing new onset CKD (HR = 0.67, 95% CI: 0.54–0.83, p < 0.001; Figure 3A ), CKD stage 3 or higher (HR = 0.55, 95% CI: 0.42–0.72, p < 0.001; Figure 3B ), and CKD stage 4 or higher (HR = 0.34, 95% CI: 0.16–0.71, p =0.004; Figure 3C ) compared with UC. These associations remained consistent after adjustment for medication use, including GLP-1 RA and SGLT2i use, whether modeled categorically (ever/never use) or continuously (proportion of days covered, PDC). Across all models, VINT participants demonstrated significantly reduced hazards: new onset CKD (categorical aHR = 0.66, 95% CI: 0.54–0.83; continuous aHR = 0.69, 95% CI: 0.55–0.86; all p<0.001), CKD stage 3+ (categorical aHR = 0.55, 95% CI: 0.42–0.72; continuous aHR = 0.58, 95% CI: 0.44–0.77; all p<0.001), and CKD stage 4+ (categorical aHR = 0.37, 95% CI: 0.18–0.78; continuous aHR = 0.36, 95% CI: 0.17–0.75; all p < 0.01) ( Figure 4 ). Sensitivity analyses further confirmed the robustness of these findings. In both the second 1:1 matched data as well as in IPTW-weighted Cox regression models yielded hazard ratios comparable in direction and magnitude to the primary analyses (Supplementary Figure 2, Supplementary Table S5 and S6). Download figure Open in new tab Download figure Open in new tab Figure 3. Cumulative incidence curves for (A) new-onset CKD (including albuminuria), (B) new-onset stage ≥3 CKD, and (C) new-onset stage ≥4 CKD during follow-up, comparing VINT participants with matched controls (UC). Curves are displayed up to 4 years to aid interpretability, given the decreasing sample size late in follow-up, though all analyses were conducted using the full 5-year observation window with standard right-censoring. At-risk tables are shown below each panel, and steps in the curves reflect incident events. Estimates toward the right tail should be interpreted with caution due to smaller denominators Download figure Open in new tab Figure 4. Hazard ratios (95% CI) for incident CKD outcomes - new-onset CKD, stage ≥3 CKD, and stage ≥4 CKD comparing treated participants with matched controls across adjustment models. Secondary Safety Outcomes Over 5 years of follow-up, the incidence rate of kidney stone diagnoses was 18.7 per 1,000 person-years among VINT participants and 22.9 per 1,000 person-years among UC. After excluding individuals with a history of kidney stones, incidence rates were 14.6 vs. 18.4 per 1,000 person-years, respectively. In unadjusted Poisson regression models, there was no statistically significant difference in kidney stone risk between groups (all participants: IRR = 0.82, 95% CI: 0.69–1.06, p = 0.05; excluding baseline cases: IRR = 0.87, 95% CI: 0.63–1.03, p = 0.07). Prevalence analyses showed similar findings, with kidney stones diagnosed in 2.5% of treated participants vs. 2.6% of controls (1.9% vs 2.1% after excluding those with baseline cases), and multiple events in 0.6% vs. 0.7%, respectively. Given the non-specific nature of ICD-10-CM E87.2 (metabolic acidosis), we prespecified acidosis and diabetic ketoacidosis (DKA) as a safety domain and report each outcome separately. The incidence of metabolic acidosis was 4.1 per 1,000 person-years in program participants versus 10.8 per 1,000 person-years in UC; in unadjusted Poisson regression the incidence rate ratio was 0.38 (95% CI, 0.28–0.51; p<0.001). DKA incidence was 7.3 per 1,000 person-years in the VINT group and 11.2 per 1,000 person-years in UC. In Cox proportional hazards models, program participation was associated with a lower risk of new-onset DKA (HR, 0.65; 95% CI, 0.51-0.83; p<0.001). The prevalence of gout was 2.4% in VINT participants and 2.7% in UC during follow-up. The incidence rate of gout was 22.8 versus 25.1 per 1,000 person-years (VINT vs UC). The unadjusted Poisson model estimated an IRR of 0.91 (95% CI, 0.78– 1.06; p=0.22), indicating no significant difference in the incidence of gout between groups. Exploratory Outcomes Among participants with mean BHB over 6 months data (n=8,066), values were distributed as follows: 0–0.3 mM in 26.6%, 0.3–0.5 mM in 31.2%, 0.5–1.0 mM in 32.5%, and ≥1.0 mM in 9.7% (Supplementary Table S3). Per the analysis plan, missing BHB values were grouped with <0.3 mM; aggregating categories, <0.3 mM (including missing) accounted for 29.4%, and ≥0.3 mM for 73.4%. For 6-month weight change, <5% loss occurred in 33.9%, 5–<10% in 27.5%, 10–<15% in 21.3%, 15–<20% in 11.3%, and ≥20% in 6.0%. In Cox proportional hazards models, mean ketone levels ≥0.3 mmol/L over 6 months were associated with a significantly lower risk of CKD in models adjusted for demographics (HR = 0.67, 95% CI: 0.48–0.92, p = 0.01; Figure 5 ). This association remained consistent after additional adjustment for medication use (HR = 0.69, 95% CI: 0.49–0.95, p = 0.02) and was also significant in a generalized linear model (OR = 0.70, 95% CI: 0.51–0.97, p = 0.03). Incidence rates of CKD were 14.9 per 1,000 person-years (95% CI: 11.3–19.2) among participants with mean ketone levels <0.3 mmol/L compared with 9.9 per 1,000 person-years (95% CI: 8.1–11.9) among those with mean ketone levels ≥0.3 mmol/L. By contrast, neither 6-month weight change (≥10% vs <10%; HR, 0.90; 95% CI, 0.72–1.13; p=0.37) nor HbA1c change at 6 or 12 months (≥0.5 percentage points vs <0.5; HR, 1.16; 95% CI, 0.91–1.49; p=0.22) was significantly associated with incident CKD; findings were similar in generalized linear models. Download figure Open in new tab Figure 5. Cumulative incidence curve for new-onset CKD during follow-up by mean ketone categories over 6 months (<0.3 vs ≥0.3 mmol/L). Curves are displayed up to 4 years to aid interpretability, given the decreasing sample size late in follow-up, though all analyses were conducted using the full 5-year observation window with standard right-censoring. At-risk tables are shown below each panel, and steps in the curves reflect incident events. Estimates toward the right tail should be interpreted with caution due to smaller denominators Among participants with baseline CKD stage and available follow-up claims data (n = 310 with 237 with stage assigned), 120 of 124 VINT participants (96.8%) demonstrated regression or stability of CKD diagnosis compared with 99 of 113 UC (87.6%). This difference was statistically significant (p = 0.005). In adjusted logistic regression models, Virta participants had a significantly greater likelihood of CKD regression or stability compared with controls (OR = 4.47, 95% CI: 1.70–15.36, p = 0.006). Detailed breakdown of medications use among participants with a CKD diagnosis at baseline and/or follow-up who had available prescription claims data are described in supplementary results section (Supplementary Table S7). Discussion Principal findings Our findings demonstrate that the VINT program was significantly associated with the primary prevention of CKD onset compared with a matched control cohort. In this large, longitudinal, claims-based analysis of participants who remained in the program for at least six months, the intervention was associated with a reduced risk of new-onset CKD (∼ 33% reduction), including diagnoses at advanced stages such as stage 3 and beyond (∼ 45% reduction), with a trend toward reduced incidence of stage 4 and beyond (∼ 66% reduction) during follow-up. These results provide novel real-world evidence that a digitally delivered lifestyle intervention emphasizing nutritional ketosis can meaningfully alter the course of CKD, a condition typically considered progressive and irreversible ( 26 ). Importantly, real-world data analyses such as this extend the relevance of clinical research by capturing outcomes in routine care settings, complementing traditional trials, and informing policy and practice decisions ( 27 ). The observation that even advanced stage CKD diagnoses were prevented, together with a significantly greater likelihood of stage maintenance and regression among participants with baseline CKD in the treated group compared to the matched control, reinforces the potential of improving kidney function in at-risk patients through nutritional and lifestyle intervention. Comparison with other studies Telemedicine and digital health interventions, including mobile applications, remote monitoring, web-based platforms, and dietitian led counseling, have consistently been shown to improve dietary quality, blood pressure, renal function, and quality of life in adults with CKD ( 28 , 29 ). Beyond clinical outcomes, telehealth enhances access to care, reduces travel and cost burdens, and supports multidisciplinary collaboration, features that are especially valuable in CKD where specialist availability is often limited. Broader reviews and models of ‘telenephrology’ also highlight increased patient satisfaction, greater adherence, and the potential for scalable, cost-effective care delivery ( 30 – 32 ). By integrating these evidence-based digital strategies into a continuous remote care platform, the intervention has been successfully applied in the reversal and management of both T2D and obesity, which are common risk factors for CKD. In this claims-based study, we present preliminary evidence that this continuous remote care platform may also be effective for preventing new onset CKD and for improving outcomes in those with underlying mild to moderate CKD. This model of care can potentially be translated and scaled to support CKD prevention and management more broadly. It is possible that the decreased onset of CKD diagnoses observed in this study reflects, in part, the unique metabolic and signalling function attributed to nutritional ketosis. Carbohydrate restriction with induction of ketosis improves insulin sensitivity, lowers systemic inflammation, reduces visceral adiposity, and improves blood pressure and glycemic control ( 18 – 23 , 33 ), all key drivers of CKD onset and progression ( 34 ). Prior post hoc and real-world analyses of carbohydrate restriction in type 2 diabetes among participants in the program have demonstrated improvements in kidney outcomes, including stabilization or reversal of eGFR slope decline ( 20 ) and significant reductions in urinary albumin to creatinine ratio (UACR) ( 23 ), suggesting a direct effect on renal hemodynamics and glomerular integrity independent of pharmacologic therapy. Our current claims-based analysis in a much larger cohort of patients further supports these findings by showing that the protective effect of the intervention on CKD prevention remained significant even after adjusting for concurrent medication use, underscoring that the observed benefit is not solely attributable to pharmacologic management. Complementary evidence from other clinical and real world studies also supports the role of carbohydrate restriction in kidney health, with reports of improvements in eGFR, UACR and cystatin C levels ( 35 – 39 ), benefits observed even with non-ketogenic levels of carbohydrate restriction, and reductions in hard renal outcomes such as risk of doubling of serum creatinine, dialysis initiation, and all-cause mortality ( 40 ). Additionally, a large observational study of individuals with CKD found that lower carbohydrate intake was associated with reduced mortality ( 41 ). In contrast, data from another study reported that higher baseline consumption of energy-dense, nutrient-poor carbohydrate sources was associated with a threefold higher risk of incident CKD over 5 years ( 42 ). These findings suggest that carbohydrate restriction, particularly nutritional ketosis, may offer greater benefit than traditional low- or very-low-protein dietary approaches. In support of this, the landmark MDRD trial demonstrated that protein restriction alone did not produce substantial or durable improvements in CKD progression or survival among individuals with moderate to advanced CKD ( 13 , 43 ). Nutritional ketosis, by targeting multiple metabolic abnormalities simultaneously, may provide a more comprehensive CKD disease modifying strategy. These observations highlight the need for a prospective randomized clinical trial (RCT) specifically designed to assess the effects of nutritional ketosis on disease progression in individuals with advanced stage CKD, particularly stages 3 and 4, where the unmet clinical need is greatest. Potential mechanisms In our survival model, participants with mean BHB levels ≥0.3 mmol/L during the first six months of the treatment were significantly less likely to develop new-onset CKD than participants with lower ketone levels, highlighting the potential kidney protective role of nutritional ketosis. This aligns with our prior post-hoc analyses, where sustained ketosis over two years demonstrated a clear dose– response relationship with improvement in eGFR slope, independent of weight loss ( 20 ). Similar findings were reported in the Liu et al. 2025 multi-cohort study of patients with diabetic kidney disease, which showed higher circulating BHB levels were linked to improved renal survival and reduced ESRD risk, with the lowest risk observed at ∼0.25 mmol/L βHB, independent of conventional risk factors and medication use ( 44 ). Mechanistically, it is plausible that the reno-protective effect of BHB may involve its known roles as a hormone-like metabolite influencing inflammation, oxidative stress, fibrosis, and renal metabolic pathways, independent of its role solely as an energy substrate ( 21 ). A limitation of the present study is the inability to assess longitudinal ketosis trajectories, as in our earlier work where sustained ketosis showed a dose–response relationship with eGFR slope improvement ( 20 ). Here, only mean ketone exposure during the first six months could be evaluated, which may underestimate the influence of longer-term nutritional ketosis on CKD outcomes. Nevertheless, the consistency across analyses reinforces the potential of nutritional ketosis to preserve kidney function, paralleling the kidney-protective effects of SGLT2 inhibitors mediated through ketonuria and βHB ( 45 , 46 ). This is further supported by findings in ADPKD, where greater ketosis was associated with both improvements in eGFR and reductions in kidney volume ( 47 , 48 ). Concerns about kidney-related safety with ketogenic interventions have historically centered on kidney stones ( 21 , 49 , 50 ) and metabolic acidosis ( 51 , 52 ). In this analysis, kidney stone risk among treated participants was comparable to matched controls in incidence, prevalence, and recurrence, which is reassuring given theoretical concerns that low-carbohydrate diets may increase nephrolithiasis risk through altered urinary calcium, uric acid, and citrate excretion. These findings suggest that in the context of medical supervision with dietary guidance and hydration support, stone risk is not elevated ( 20 ). Safety evaluation extended beyond nephrolithiasis. Despite perceptions of ketogenic diets as “high protein,” this intervention used moderate protein intake consistent with recommended allowances, and treated participants exhibited lower rates of acidosis. This reduction may reflect preserved kidney function, which mitigates metabolic acidosis, particularly in advanced CKD ( 20 , 52 ). Because ICD-10 coding for acidosis can overlap with DKA, we examined DKA separately and also observed lower incidence, likely reflecting preserved kidney function and improved glycemic control, although nuances between these outcomes cannot be fully disentangled. Together, these results underscore that physiologic nutritional ketosis remains within a safe, regulated range and is distinct from pathologic ketoacidosis ( 53 ). Finally, given concerns about gout related to renal urate handling and reports of transient urate increases with initiation ( 54 ), we evaluated gout as a safety endpoint. Neither follow-up prevalence nor incident gout was higher in the treated group, and mechanistic evidence suggests that β-hydroxybutyrate may even relieve flares by deactivating the NLRP3 inflammasome ( 55 ). Strengths and limitations of study This study has notable strengths, including use of a large external claims’ dataset with up to 5 years of follow-up, robust propensity score–based matching, and confirmation through IPTW sensitivity analysis. While there were mild differences in the use of medications with kidney indications or potential kidney benefits between the two cohorts, the analysis accounted for both categorical and continuous measures of these therapies, and the effect of VINT on the onset of CKD diagnoses remained robust across analytic approaches, strengthening confidence in the results. Limitations include reliance on closed claims data, which restricted follow-up to about 10% of the original cohort, although the final analytic sample was broadly representative of program participants. Outcomes were defined using ICD-10 codes rather than laboratory values, which may introduce misclassification and prevent direct assessment of eGFR or albuminuria. It is also possible that some diagnoses were under-identified due to delayed access to care or insufficient follow-up; however, these limitations are likely to be similar across both the treated and control groups. Some cases classified as advanced CKD may reflect earlier disease diagnosed before the one-year pre-index period, and the analysis was limited to primary CKD onset rather than secondary progression of established stage 3 or 4 disease. As with all observational research, residual confounding from unmeasured factors (e.g., lifestyle behaviors, health-seeking tendencies) cannot be excluded. Future randomized controlled trials are needed to confirm whether nutritional ketosis can slow or reverse advanced CKD, incorporating both kidney surrogate markers (e.g., eGFR slope, albuminuria) and hard outcomes such as MAKE, dialysis, transplantation, and mortality. Conclusions While RCTs remain the gold standard, our findings provide complementary real-world evidence that not only extends generalizability to broader, more heterogeneous patient populations but also demonstrates effectiveness in routine care settings, offering timely insights to inform clinical guidelines, payer decisions, and health policy ( 27 ). These findings reinforce that the program is not merely a treatment for type 2 diabetes reversal and weight loss, but a comprehensive metabolic therapy capable of directly altering the trajectory of kidney disease risk and progression. This treatment approach, already established as an evidence-based model for achieving diabetes reversal and sustained weight loss, integrates continuous remote monitoring, medication management, and individualized dietary support focused on carbohydrate restriction. Delivered digitally, this model offers a scalable, nonpharmacologic strategy to help reduce the growing burden of CKD among populations at metabolic risk. Data Availability Statement The datasets analyzed in this study are not publicly available because they were obtained through a commercial license from the data vendor. Access to these data is restricted, and they were used solely under the terms of the license granted for this study. Compliance with Ethics Guidelines The study was determined to be exempt from institutional review board (IRB) review, as it relied solely on retrospectively collected, de-identified data and did not involve any additional contact or intervention with human subjects. Reporting guidelines This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for cohort studies. A completed STROBE checklist was prepared and submitted with the manuscript. Funding This study did not require external funding, as it was a retrospective observational analysis based on deidentified claims data. No specific grant from any funding agency in the public, commercial, or not-for-profit sectors was received for this research. Competing interests Three authors (SJA, PVS, AW) are employees of Virta Health and offered stock options. One author (JSV) is a co-founder and shareholder of the company. Virta Health provides a remote care intervention including individualized nutrition therapy for people with type 2 diabetes and related metabolic conditions. These authors and other coauthors contributed to the study design, data analysis, interpretation, and manuscript preparation. All other authors declare no competing interests related to the data source, data analysis, interpretation, or reporting of this study. Author contributions SJA drafted the manuscript, and contributed to the study concept and design, critically reviewed and revised the manuscript, and approved the final version. SJA performed the statistical analyses. SJA and PVS acquired and curated the data. RJJ, PB, AW, TW, JH, and JSV provided clinical and scientific oversight and interpretation of the findings. All authors reviewed and revised the manuscript. RJJ supervised the study and is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Transparency The lead author, and the guarantor, confirms that this report is an honest, accurate, and transparent account of the study. All significant elements have been presented. Dissemination The findings of this study will be disseminated to the public through social media, blogs, and professional networks to enhance accessibility and public understanding. For research transparency and early feedback, the manuscript has been uploaded as a preprint [insert DOI here]. Once the study is published, we plan to share a summary of the results through digital platforms and academic forums. The study and its findings will also be used to support future research proposals and grant applications aimed at expanding this work and evaluating long-term kidney outcomes in broader populations. References 1. ↵ Ding X , Li X , Ye Y , Jiang J , Lu M , Shao L . Epidemiological patterns of chronic kidney disease attributed to type 2 diabetes from 1990–2019 . Front Endocrinol (Lausanne) 2024 ; 15 : 1383777 . doi: 10.3389/fendo.2024.1383777 OpenUrl CrossRef PubMed 2. ↵ Francis A , Harhay MN , Ong ACM , et al. 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Share Effectiveness of telehealth nutritional therapy in preventing chronic kidney disease among adults with type 2 diabetes and obesity: a real-world, retrospective, propensity score–matched cohort study Shaminie J Athinarayanan , Petter Bjornstad , Priya V Shanmugam , Thomas Weimbs , Adam Wolfberg , Jeff S Volek , Jonathan Himmelfarb , Richard J Johnson medRxiv 2025.10.17.25338238; doi: https://doi.org/10.1101/2025.10.17.25338238 Share This Article: Copy Citation Tools Effectiveness of telehealth nutritional therapy in preventing chronic kidney disease among adults with type 2 diabetes and obesity: a real-world, retrospective, propensity score–matched cohort study Shaminie J Athinarayanan , Petter Bjornstad , Priya V Shanmugam , Thomas Weimbs , Adam Wolfberg , Jeff S Volek , Jonathan Himmelfarb , Richard J Johnson medRxiv 2025.10.17.25338238; doi: https://doi.org/10.1101/2025.10.17.25338238 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Nephrology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (541) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00869d4cb7e0db4',t:'MTc3OTU4NDk1MA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();