Preclinical and clinical obesity: prevalence, associations to cardiometabolic risk and response to lifestyle intervention in NHANES and the EPIC-Potsdam and TULIP studies

Domain: chemistry
Published: 2026-02-19
Editorial outcome: minor_revision

Abstract
An expert commission (The Lancet Diabetes & Endocrinology Commission on Clinical Obesity) proposed novel diagnostic criteria distinguishing between preclinical and clinical obesity and suggesting treatment indications for the latter. However, the proportional assignment to preclinical and clinical obesity in adults with BMI-defined obesity, the associated disease risks, as well as the response to lifestyle interventions are not well known. Here we show that among those with BMI-based obesity, 100% are confirmed to have obesity by at least one other anthropometric measure in NHANES 2017-2018 and the prospective EPIC-Potsdam cohort. More than 80% of adults with confirmed obesity meet the criteria for clinical obesity and have 2.8-fold increased risk of incident cardiovascular disease and 7.9-fold increased risk for type 2 diabetes compared to adults without obesity and not fulfilling clinical criteria. Adults with preclinical obesity have no elevated cardiovascular disease risk, but type 2 diabetes risk is markedly increased. A 9-months lifestyle intervention (Tübingen Lifestyle Intervention Programme) decreases the proportion of clinical obesity from 71% to 57%, and that of prediabetes from 52% to 29%.

Introduction
Obesity is a leading cause of disability and mortality1. While obesity has been associated with increased risk of various health problems, the question of whether obesity per se reflects a disease has been a matter of debate. A Lancet Diabetes & Endocrinology Commission on the definition and diagnostic criteria of clinical obesity (hereinafter referred to as ‘Commission’) has recently proposed diagnostic criteria for preclinical and clinical obesity2. As a first step, diagnosing obesity requires supplementary anthropometric measures or direct body fat assessment to confirm a BMI-based classification. We refer to “confirmed obesity” throughout the manuscript to reflect this initial step. Individuals with confirmed obesity can subsequently be classified into “clinical obesity” if signs, symptoms, or diagnostic tests show abnormalities in the function of one or more tissue or organ systems due to obesity, or if substantial limitations of daily activities due to obesity exist. Individuals with confirmed obesity who do not meet criteria for clinical obesity are categorized as having “preclinical obesity”. The attempt to address the contentious issue of “obesity as a disease” aims to enhance treatment guidance and reduce social stigma but presents several challenges. First, in the general population high BMI strongly correlates with elevated body fatness and abdominal obesity3,4 and recent data from US adults suggest that almost all with BMI-based obesity are confirmed to have obesity5, which questions the requirement to confirm a BMI-based obesity classification by additional measure of body fatness. Second, individuals with high BMI often exhibit metabolic or clinical manifestations; for example, only 10% of those classified as having obesity by BMI in the National Health and Nutrition Examination Survey (NHANES) III lacked any component of the metabolic syndrome6. If additional obesity-related symptoms are considered to diagnose clinical obesity, few individuals with obesity would be categorized as having preclinical obesity. However, representative estimates on relative proportions of preclinical and clinical obesity are so far lacking. Also, implementing the new classification in clinical practice requires empirical data supporting its value for long-term health outcomes. Consequences of classifying obesity into preclinical and clinical obesity in terms of cancer risk have recently been investigated7,8, but remain unknown for cardiovascular disease (CVD) and type 2 diabetes (T2D) risk. Clinical obesity may harbor subgroups of substantially different cardiometabolic risk and different requirements and goals of intervention, but evidence necessary for further staging and assignment of treatment is unavailable yet. To characterize respective responses to weight-loss and evaluate the parameters that predict the remission of clinical obesity, data from weight loss intervention studies are required. This information is critical for evaluating the clinical and health benefits of implementing these diagnostic criteria in practice.

In this work, we provide U.S.-nationally representative estimates of the proportion of preclinical and clinical obesity among adults, analyze consequences of this classification for future CVD and T2D risk and investigate response patterns to a lifestyle intervention.

Results
Analyses framework and study characteristics
We investigated confirmation of a BMI-based obesity classification by other anthropometric measures and the proportion of individuals with confirmed obesity classified as clinical in NHANES 2017-2018 and the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study (Fig. 1a). NHANES participants (n = 5265) had a mean age of 48.3 years, 48.1% were men, 62.2% non-Hispanic White, and 11.5% non-Hispanic Black. EPIC-Potsdam participants were largely women (61%) with a mean age of ~50 years and were exclusively white Europeans (Supplementary Table 1). We linked the clinical obesity status to future cardiometabolic disease risks using case-cohorts nested within EPIC-Potsdam (Fig. 1b). We furthermore investigated the potential remission from clinical obesity by lifestyle intervention in the Tübingen Lifestyle Intervention Programme (TULIP) study over 9 months (Fig. 1c).

Confirmation of BMI-based obesity by further anthropometry
Among adults with obesity defined by BMI, the proportions being confirmed by individual additional body fat measures ranged between 91.6% (dual-energy X-ray absorptiometry, DXA) and 99.9% (waist-to-height ratio) in NHANES 2017-2018 and from 73.1% (waist-to-hip ratio) and 100% (waist-to-height ratio) in EPIC-Potsdam. Obesity status was confirmed by at least one additional measure in almost all adults with BMI-based obesity (Supplementary Table 2). The proportion of BMI-based obesity confirmed by DXA or waist circumference was higher in the higher BMI categories, with almost all with a BMI-based class II or III obesity being confirmed by waist circumference.

Proportional fulfillment of individual clinical obesity criteria
The estimated proportion of US adults (NHANES 2017-2018) with confirmed obesity fulfilling individual diagnostic criteria of clinical obesity ranged from 10.6% (95% CI, 8.7%–12.6%) for the activities of daily living (ADL) criteria to 71.1% (95% CI, 67.8%–74.4%) for the cardiovascular criteria (Fig. 2). While 18.9% of adults with confirmed obesity fulfilled just a single diagnostic criterion, 20.1% fulfilled 2, 16.9% fulfilled 3 and even 6.9% fulfilled 6 or more criteria simultaneously (Supplementary Table 3). Overlap of adults fulfilling different diagnostic criteria was largest for the cardiovascular and respiratory criteria in NHANES (Fig. 2). In EPIC-Potsdam, 90.7% fulfilled the cardiovascular component, while 21.2% fulfilled the metabolic component. Overlap was highest for the cardiovascular and metabolic component (20.7%) (Fig. 2). The proportion of adults with confirmed obesity fulfilling individual clinical criteria of clinical obesity varied across age, BMI, gender, and race and ethnic subgroups in NHANES (Supplementary Table 4).

Prevalence of clinical obesity
The estimated prevalence of clinical obesity, thus of individuals with confirmed obesity who met at least one clinical criterion, in the US adult population was 36.0% (95% CI, 33.2–38.9%) and of preclinical obesity 7.6% (6.2–9.0%) (Table 1). The proportion among adults with confirmed obesity in the US who fulfilled any diagnostic criterion of clinical obesity was 82.6% (79.8–85.4%). This proportion increased with increasing BMI (class I obesity: 78.7%, class II obesity: 84.3%, class III obesity: 90.5%) and age (age 20–44 years: 69.0%, 45–64 years: 89.5%, ≥65 years: 99.0%), was comparably high in females (82.3%) and males (83.0%), and was higher in non-Hispanic Whites (85.7%) and non-Hispanic Blacks (84.1%) than other ethnic/racial groups. In EPIC-Potsdam, the proportion of confirmed obesity being clinical was 92.1%, which increased with higher BMI and age, similar to NHANES (Table 1).
Among adults without confirmed obesity, the estimated proportions fulfilling one or more diagnostic criteria for clinical obesity were 63.3% in NHANES 2017-2018 and 72.6% in EPIC-Potsdam, which differed depending on BMI (Supplementary Table 5).

Impact of varying the metabolic component
While the Commission defined the metabolic component as “cluster of hyperglycemia, high triglyceride, and low HDL-cholesterol”, this definition likely excludes many individuals with metabolic clinical obesity. We therefore repeated the analyses, modifying the metabolism criterion of clinical obesity (Supplementary Table 6). The proportion of adults with confirmed obesity classified as having clinical obesity varied strongly between different criteria. If any of the metabolic components were considered to indicate clinical obesity, the proportions of adults with confirmed obesity being clinical were 96.3% in NHANES and 98.5% in EPIC-Potsdam. Similar to the main analysis, the proportions of individuals with confirmed obesity classified as clinical were usually higher with increasing BMI level.

Clinical obesity and risk of incident T2D and CVD
Given that several clinical obesity criteria reflect major cardiometabolic risk factors, we next evaluated associations of preclinical and clinical obesity with risk of T2D and CVD in the EPIC-Potsdam study. Participants with clinical obesity had a substantially higher risk for T2D compared to participants without obesity not fulfilling clinical criteria (HR: 7.88, 95% CI: 5.56–11.2). Still, the risk was also high among participants with preclinical obesity (4.32, 1.77–10.5), and even individuals without obesity but who fulfilled clinical criteria had elevated risk (Fig. 3). With regard to CVD, preclinical obesity was not related to risk. In contrast, a more than doubled risk was observed among participants with clinical obesity (HR: 2.78, 95% CI: 1.78–4.34). Noteworthy, the risk among participants without obesity who fulfilled clinical criteria was substantially higher as well (2.21, 1.49–3.26).
The strict definition of the metabolic component (hyperglycemia in combination with low HDL-cholesterol and high triglyceride levels) likely assigns a preclinical rather than clinical state to many individuals who already show body fatness-related metabolic dysfunctions. We therefore repeated these analyses, using a reference group of individuals without obesity who were free of any metabolic signs (individuals with any metabolic alteration were included in the non-obese clinical group). HRs increased for CVD for all groups which met clinical criteria compared to the initial analysis (Supplementary Table 7).

Response to lifestyle interventions
We then investigated in TULIP the effects of weight loss on the change of the proportion of clinical obesity, the changes of the individual components (hypertension, metabolic, and renal) that were used for the diagnosis of clinical obesity, and selected parameters that may predict remission of clinical obesity. In 150 people with a BMI ≥ 30 kg/m2 the median weight loss during 9 months of the lifestyle intervention was 3.0% and people were then divided into a group with ≤ 3% weight loss (n = 75) and a group with > 3% weight loss (n = 75). Participants of the group that lost ≤ 3% of body weight had a median weight change of -0.99% during 9 months of follow-up (Supplementary Table 8). At baseline, 65% of these participants had clinical obesity and most of them were classified with clinical obesity based on the cardiovascular (hypertension) criterion. A total of 13% fulfilled the metabolic criterion. Only few anthropometrics and clinical parameters improved during the intervention. Remission of clinical obesity was observed in 9 out of 49 participants, and as a result, the proportion of clinical obesity decreased from 65% to 61%. A decrease of the cardiovascular and metabolic, but not the renal components of clinical obesity, that were used for the definition of clinical obesity, were observed. Among selected parameters, only age was associated with remission of clinical obesity, with borderline significance (Supplementary Table 9).
Participants of the group that lost > 3% of body weight had a median weight change of -7.4% (Table 2). At baseline, 71% of the participants had clinical obesity, with most meeting the cardiovascular (hypertension) criterion. A total of 13% fulfilled the metabolic criterion. Most anthropometrics and clinical parameters improved during the intervention. Remission of clinical obesity was observed in 14 out of 53 participants and, as a result, the proportion of clinical obesity decreased from 71% to 57%. A decrease of all three components of clinical obesity that were used for the definition of clinical obesity (cardiovascular, metabolic, and renal) was observed. Noteworthy, among the components of the metabolism criterion, substantial reductions in triglyceride levels (from 148 to 118 mg/dl, -20%) and the proportion with prediabetes (from 52% to 29%) were observed, while HDL-cholesterol levels did not change. Among selected parameters, higher age, higher liver fat content and higher visceral fat mass were associated with a lower chance of remission of clinical obesity, while gender, BMI at baseline or change in weight were not significantly associated. In addition to age, liver fat content, but not visceral fat mass, explained large part of the variability in remission of clinical obesity (Supplementary Table 10).
Sensitivity analyses grouping participants according to a 5% weight loss-cutoff (n ≤ 5% weight loss = 104, n > 5% weight loss = 46) showed mostly comparable results (Supplementary Tables 11–14). However, the small size of the > 5% weight-loss subgroup limits the conclusions that can be drawn.

Methods
NHANES 2017-2018
NHANES is a series of nationally representative surveys of US adults. Beginning in 1999, NHANES has been a continuous, multistage, nationally representative survey of the noninstitutionalized civilian resident US population. Data collected through in-home interviews and study visits at mobile examination centers have been released in 2-year cycles. This study included the 2017-2018 cycle due to the availability of measures reflecting obesity (weight, height, waist and hip circumference, DXA-measured body fat percentage) and diagnostic criteria of clinical obesity. The unweighted overall response rate was 51.9% for the interview component and 48.8% for the examination component. 5265 participants aged 20 years or older were included. The National Center for Health Statistics Research Ethics Review Board approved NHANES. Written informed consent was obtained from all adult participants.
Information on age, gender, race and ethnicity, medication use, and medical conditions was collected during household interviews according to fixed-category questions. Race and ethnicity were included because of known racial and ethnic differences in obesity prevalence. Weight, height, waist circumference, hip circumference, body fat, blood pressure, and liver stiffness were measured in mobile examination centers with standard protocols by trained personnel. The mean of the second and third available blood pressure measurements was used to calculate systolic and diastolic blood pressure. The percentage of body fat was measured using the Hologic Discovery model A densitometers (Hologic, Inc., Bedford, Massachusetts), software version Apex 3.2. and analyzed with Hologic APEX version 4.0 software. Liver stiffness was measured via FibroScan® model 502 V2 Touch. HbA1c was measured via HPLC. HDL-cholesterol levels were directly measured. Triglyceride and glucose levels were measured through enzymatic methods in a random subset of the participants who fasted for 8 to less than 24 h. Urine albumin and creatinine levels were measured with a fluorescent immunoassay and the Jaffe rate reaction method, respectively. Serum creatinine levels were measured using the Jaffe kinetic rate method. Estimated glomerular filtration rate (eGFR) was computed according to the Chronic Kidney Disease Epidemiology Collaboration equation18. FIB-4 and the NAFLD fibrosis score were calculated based on age, alanine aminotransferase, and aspartate aminotransferase (both measured via kinetic rate reaction), platelet count (measured using the Beckman Coulter DxH 800 instrument), and, for the NAFLD fibrosis score, additionally albumin and BMI19,20. Detailed descriptions of the assessment methods are publicly accessible21.

EPIC-Potsdam study
The EPIC-Potsdam study is a prospective cohort that recruited from 1994 to 1998 27,548 participants (16,644 women and 10,904 men; primarily of Middle European ancestry, age range: 35–65 years) from the general population of Potsdam and surroundings, Germany22. Follow-up occurred every 2–3 years via mailed questionnaires, with response rates ranging from 90% to 96%. The study protocol was approved by the ethics committee of the Medical Society of the State of Brandenburg, Germany, and all participants provided a statement of written informed consent before enrollment.
Incident CVD was defined as the incidence of primary nonfatal and fatal MI and stroke (International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes: I21 for acute MI, I63.0 to I63.9 for ischemic stroke, I61.0 to I61.9 for intracerebral hemorrhage, I60.0 to I60.9 for subarachnoid hemorrhage and I64.0 to I64.9 for unspecified stroke). Incidence of CVD was captured by participants’ self-reports or based on information from death certificates. Information on the incidence of T2D (ICD-10 code E11) was systematically acquired through self-report of a diagnosis, T2D-relevant medication, or dietary treatment due to a T2D diagnosis during follow-up. Additionally, death certificates and information from tumor centers, physicians or clinics that provided assessments for other diagnoses were screened for indication of incident T2D. For participants who were classified as potential cases based on that information, standard inquiry forms were sent to the treating physician to provide diagnostic details on CVD and T2D. Only physician-verified cases with a diagnosis date after the baseline examination were considered confirmed incident cases of CVD and T2D.
Nested case–cohorts were constructed for efficient study of molecular phenotypes. From all participants who provided blood at baseline (n = 26,437), a random sample (subcohort, n=2500) was drawn, which served as the population for estimating proportions with confirmed obesity and clinical obesity (n = 2391 after excluding individuals where the clinical obesity state could not be determined due to missing information) and as a common reference population for both T2D and CVD endpoints. For each endpoint, all incident cases that occurred in the full cohort until a specified censoring date (30 November 2006 for CVD, 31 August 2005 for T2D) were included in the analysis. We excluded participants with prevalent or non-verifiable outcomes (n = 124, n = 59), possible CVD cases according to WHO MONICA criteria (n = 38), self-reported diabetes or diabetic medication at baseline (n = 60), missing follow-up information (n = 56, n = 60), missing BMI (n = 5, n = 5) or where fulfillment of clinical criterion could not be determined (n = 119, n = 131). The final CVD case-cohort sample comprised 2606 participants, including 403 cases (48 internal). The final T2D case-cohort sample comprised 2904 participants, including 785 cases (73 internal).
Follow-up was defined as the time between enrollment and study exit, determined by diagnosis of the respective disease, death, dropout, or final censoring date, whichever came first.
Information on administrative sex and date of birth of the participants was forwarded by the residence registry. Anthropometric and blood pressure measurements were conducted according to standardized protocols23,24. Information on lifestyle and education was obtained using questionnaires and computer-assisted personal interviews. These included information on recreational physical activity, smoking status, average alcohol intake, diet, and educational attainment. Blood was partitioned into serum, plasma (with 10% of total volume citrate) and blood cells and was subsequently separately stored in tanks of liquid nitrogen at -196°C or in deep freezers at -80°C until the time of analysis. HbA1c and plasma concentrations of HDL-cholesterol, triglycerides, and glucose were measured at the Department of Internal Medicine, University of Tübingen, with an automatic ADVIA 1650 analyzer (Siemens Medical Solutions) in 2007. All biomarker measurements conducted in plasma were corrected for the dilution introduced by citrate volume to improve comparability with concentrations measured in EDTA-plasma reported in the literature.

TULIP study
Individuals at high risk for T2D from the southern part of Germany participated in TULIP, a prospective uncontrolled diet and exercise intervention study (supported by the German Research Foundation (DFG) and registered on their website - https://gepris.dfg.de/gepris/projekt/5396893?language=en). The study was performed at the University Hospital of Tübingen, Germany. The study started in 2003 and the lifestyle intervention of the last patient included in the study ended in 2009. Outcomes of the study – e.g. glucose tolerance, insulin sensitivity, insulin secretion, body fat distribution, liver fat content, adipokines, hepatokines, genetic risk of T2D etc. – have been published25–27. People were included in the study when they fulfilled at least one of the following criteria: a family history of T2D; BMI > 27 kg/m2; impaired glucose tolerance (2-h blood glucose ≥ 7.78 mmol/l); or previous diagnosis of gestational diabetes. They were considered healthy according to a physical examination and routine laboratory tests. The participants had no history of liver disease and did not consume more than 2 alcoholic drinks per day. Exclusion criteria were the diagnosis of diabetes mellitus or the presence of a severe, critical mental or physical illness. Informed written consent was obtained from all participants and the Ethics Committee of the University of Tübingen, Germany, approved the protocol.
A total of 413 individuals were enrolled. For this analysis, we included TULIP participants who had a BMI ≥ 30 kg/m2 at baseline, with available information on diagnostic criteria for clinical obesity, who underwent 9 months of intensive lifestyle intervention (n=150). At baseline, gender was assessed via self-report, medical history was obtained, and a physical examination was performed. Phenotypic characterization was performed using a 75g OGTT, whole-body MRI and H-MRS, as well as a cycle exercise test. This examination was repeated after 9 months. After the baseline examination, all participants underwent monthly sessions of standardized individual face-to-face dietary counselling by trained diabetes educators. Counselling aimed to reduce body weight by ≥ 5%. Furthermore, participants were advised to reduce fat intake to < 30% of total energy consumed and to increase dietary fiber intake to at least 15 g/4185 kJ (1000 kcal). They were also encouraged to reduce the intake of saturated fatty acids to < 10% of total fat. During each visit, participants presented a 3-day food diary and discussed it with the dietitians. Diet composition was estimated with validated software. Individuals were motivated to perform at least 3 h of moderate exercise per week. A heart rate monitor was given to the participants, and they were instructed to exercise so that their heart rate was just below their individual anaerobic threshold. Individual anaerobic threshold was measured by a motorized treadmill.

Definition and confirmation of obesity
BMI was calculated by dividing weight in kilograms by height in meters squared. The waist-to-hip ratio was calculated as the ratio of waist and hip circumference (both in cm) and the waist-to-height ratio as the ratio of waist circumference and height (both in cm). Similar to Aryee et al.5, we used ethnic-specific cut-offs for BMI and waist circumference according to the Commission report2 and World Health Organization28–30. Obesity was defined as a BMI ≥ 30 kg/m2 except for non-Hispanic Asian, for whom obesity was defined as BMI ≥ 27.5 kg/m2. Accordingly, obesity class I was defined as BMI 30.0–34.9 kg/m2 (27.5–32.4 kg/m2 in non-Hispanic Asian), obesity II as BMI 35.0–39.9 kg/m2 (32.5–<37.5 kg/m2), and obesity class III as BMI ≥ 40.0 kg/m2 (≥ 37.5 kg/m2). Following the Commission report on clinical obesity2, we confirmed high body fatness among those meeting the BMI threshold of obesity using additional anthropometric measurements: waist circumference (for non-Hispanic Asians ≥ 80 cm for women and ≥ 90 cm for men, otherwise ≥ 88 cm for women and ≥ 102 cm for men), waist-to-hip ratio (> 0.85 for women, > 0.90 for men), waist-to-height ratio (> 0.5 for both sexes and genders), and DXA-based body fat percentage (> 35% for women, > 25% for men)31,32. Participants with BMI-based obesity who met any of the above criteria were considered to have “confirmed obesity”.

Definition of preclinical and clinical obesity
Factors to categorize individuals with confirmed obesity into either preclinical or clinical obesity were based on those proposed by the Commission2. A detailed list of indications of obesity-related organ or tissue dysfunction proposed by the Commission and the corresponding information available in NHANES 2017-2018 and EPIC-Potsdam is provided in Supplementary Table 15. Briefly, in NHANES we included signs and symptoms related to 1) upper airways: adapted STOP-Bang score33, 2) respiratory system: self-reported shortness of breath, 3) cardiovascular system: self-reported heart failure, elevated measured blood pressure, self-reported antihypertensive medication, or self-reported shortness of breath 4) metabolism: hyperglycemia (self-reported diabetes, impaired fasting glucose or high HbA1c) combined with low HDL-cholesterol levels and high triglyceride levels or lipid-lowering medication 5) renal: microalbuminuria (urinary albumin-creatinine ratio ≥ 30 mg/mmol) or reduced eGFR (< 60 mL/min per 1.73 m2)34 or received dialysis in past 12 months, 6) urinary system: self-reported chronic urinary incontinence, 7) liver: hepatic fibrosis (FIB-4 index35,36, increased NAFLD Fibrosis Score19,35, or increased hepatic stiffness measured by vibration-controlled transient elastography (FibroScan®)), 8) musculoskeletal: self-reported osteoarthritis, and 9) self-reported limitations of activities of daily living (ADL) if reported to be due to weight problems. Indications of obesity-related dysfunction specific to the central nervous, reproductive, and lymphatic systems were unavailable. In EPIC-Potsdam, we included 1) cardiovascular system: self-reported heart failure, elevated measured blood pressure (> 130/80 mmHg), or self-reported antihypertensive medication 2) metabolism: hyperglycemia (self-reported diabetes, impaired fasting glucose or high HbA1c) combined with low HDL-cholesterol levels [or self-reported lipid-lowering medication] and high triglyceride levels [or self-reported lipid-lowering medication], 3) renal: reduced eGFR34, and 4) reproductive: tried to become pregnant for more than one year in females, sought medical consultation on fertility in males.
In the TULIP study, we considered the following clinical criteria: 1) cardiovascular system (blood pressure > 130/80 mmHg or antihypertensive medication), 2) metabolism (impaired fasting glucose, impaired glucose tolerance or high HbA1c [prediabetes] combined with low HDL-cholesterol levels [or self-reported lipid-lowering medication] and high triglyceride levels [or self-reported lipid-lowering medication]), and 3) renal (reduced eGFR [< 60 ml/min/1.73 m2]34).

Statistical analysis
The NHANES 2017-2018 cycle was used to estimate prevalences of confirmed obesity and of preclinical and clinical obesity, overall and by age (20–44, 45–64, and ≥ 65 years), gender (men and women), and race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic overall, Mexican American as a separate Hispanic subgroup, non-Hispanic Asian, and other). Stratified analyses according to previously described subgroups and the presence of clinical obesity (yes/no) were conducted. We also estimated proportions among adults across different BMI categories who fulfilled single or multiple diagnostic criteria for clinical obesity. These analyses accounted for the complex sampling design through inclusion of survey weights and design variables according to NHANES analytic guidelines21 to obtain weighted estimates representative of the total civilian noninstitutionalized US population. Missingness in the medical examination sample in variables relevant for analyses ranged from 0.9% in body weight to 34.6% in DXA-measured total body fat (Supplementary Table 16). As recommended by the NHANES analytic guideline21, multiple imputation (m=5) was applied using random forest to approximate missing data in the medical examination sample. Survey-weighted estimates were summarized using Rubin’s rules considering intra- and inter-imputation variability.
In EPIC-Potsdam, we estimated proportions among adults who fulfilled criteria for confirmed obesity, using BMI and additional anthropometric measures, and who fulfilled single or multiple diagnostic criteria for clinical obesity using the random subcohort. We assessed the association between preclinical and clinical obesity and incident CVD and T2D with Cox proportional hazards regression within the case-cohorts, accounting for the design by assigning weights as proposed by Prentice and handling ties by Efron approximation. Age was the underlying timescale, with entry time as age at baseline and exit time as age at event or censoring. The fully adjusted model included age (as underlying timescale) sex, sport and cycling (hours/week), smoking status (never, former, current light < 20 units/day, current high ≥ 20 units/day), educational status (current in training/no certificate/part, skilled worker, professional school, college or higher education/university), alcohol consumption (never, former, current ≤ 12 g (women)/≤ 24 g (men), current > 12 g (women)/> 24 g (men)), consumption of whole grain, red meat, and coffee (all in g/day) as covariates.
In TULIP, differences between baseline and follow-up data were tested using the matched pairs t-test for continuous data and the Fisher’s exact test for categorical data. Remission of clinical obesity, thus moving from clinical obesity (confirmed obesity with at least one clinical criterion met) at baseline to a state without meeting any clinical criterion at follow-up, was first investigated in univariate nominal logistic regression models. The parameters of interest were gender, age, BMI, visceral fat mass, and liver fat content at baseline and change in body weight, which we found to be important determinants of metabolic health in obesity12. Remission of clinical obesity was then investigated in multivariable nominal logistic regression models that included parameters with a statistically significant univariable association with remission. A stepwise forward logistic regression model was used to determine the variability in remission of clinical obesity explained by its determinants.
Data were analyzed with SAS (version 9.4), R Studio (version 2023.3.0.386), and JMP 16.2.0 (SAS Institute Inc, Cary, NC, USA).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors responses to reviewer comments were adequate. Specifically, we want to highlight the appropriateness and\npotential areas for improvement for some of the responses as part of this review.\n1. We agree with the reviewer’s concern that the paper appeared fragmented without a clear rationale for why those specific\ndatasets (NHANES, EPIC, and TULIP) and not others were used was adequately addressed by the authors’ response. They\nnow explicitly link each dataset (NHANES for prevalence, EPIC-Potsdam for prospective risk, TULIP for intervention\nresponse) to the corresponding study aims and provide supporting references to prior related work. The revised manuscript\nreflects this structure clearly, and I find the response satisfactory.\n2. The reviewer wanted a stronger rationale for Aims 2 and 3, noting that the metabolic healthy/unhealthy obesity literature\nalready addresses a similar concept. The authors have acknowledged the overlap with the metabolic healthy/unhealthy\nobesity literature but appropriately clarified that their scope is to evaluate the newly proposed Lancet DE Commission’s\ndefinition of preclinical/clinical obesity. While comparison with other classifications is of interest, it is indeed beyond the\nscope of this paper. The revised Introduction now more clearly justifies Aims 2 and 3 in terms of assessing long-term\ncardiometabolic risk and treatment response. I find the response satisfactory.\n3. The reviewer raised a concern about novelty since part of the analysis had already been published in a JAMA paper. The\nauthors acknowledge that part of their analysis (confirmation of obesity in NHANES) replicates findings from Aryee et al. in\nJAMA. However, they clearly distinguish the novel contribution of their study in applying the second and third aims. They\nhave also moved the overlapping material to the supplement to keep the main focus on new results. I find this a reasonable\nand satisfactory response.\n4. These changes address the reviewer’s concerns and improve methodological consistency. I find the responses\nsatisfactory.\n5. The authors have clarified the definitions and corrected a small error in the renal criteria, specified that limitations in daily\nactivities were only included when explicitly linked to weight problems (noting the very small impact of this component), and\nacknowledged the limitations in available measures within NHANES and EPIC. They appropriately frame the latter as both\na limitation and a reflection of real-world challenges in implementing the new classification. I find the responses satisfactory.\n6. The reviewer's critique seems to conflate the purpose of this study with the novelty of the concept itself. The “clinical\nobesity” classification was proposed by the Lancet DE Commission, not by these authors, and the goal of this paper is to\nevaluate that framework empirically. The authors acknowledge overlap with prior metabolic healthy/unhealthy obesity\nresearch but clarify that their aim was to evaluate the new Lancet DE Commission classification of clinical obesity, which\nuses distinct criteria. In this context, demonstrating the risk associations of the new classification is an important and novel\ncontribution, even if the general direction of findings is consistent with prior literature. I find the response satisfactory.\n7. The reviewer’s suggestion is reasonable — obesity is strongly related to heart failure (HF), often more than stroke/MI, so\nincluding HF as an outcome could strengthen the analysis. The authors argue that heart failure cannot be evaluated\nprospectively because it is already part of the clinical obesity criteria, which I agree would create overlap if prevalent HF\nwere included at baseline. However, incident HF occurring after baseline could still provide valuable information, given the\nstrong relationship between obesity and HF. If such data are unavailable, this limitation could be acknowledged more\nexplicitly.\n8. The reviewer flagged the case-cohort design itself as a limitation. The authors appropriately clarify that the case–cohort\ndesign is a well-established and efficient method for prospective analyses, and not a limitation per se. They now\nacknowledge that the limited sample size of the sub-cohort may affect the precision of prevalence estimates in EPIC-\nPotsdam, which is a reasonable framing. This is a solid response.\n9. The reviewer pointed out that there are larger and more established CVD cohorts that might be better suited for the\nanalyses done by the authors. The authors acknowledge that those cohorts exist and are valuable. They then argued that\nthose cohorts do not necessarily capture all the clinical criteria needed for the Lancet DE Commission’s definition of clinical\nobesity. Their stance is reasonable as the purpose here isn’t just to study CVD in general — it’s to evaluate the new\ndefinition of clinical obesity. That requires data on both anthropometric and metabolic dysfunction criteria, which are not\nsystematically collected in all cohorts.\n10. The reviewer is concerned that using T2D as an outcome might seem circular, since diabetes (or hyperglycemia) is part\nof the criteria for clinical obesity. The authors included self-reported T2D as part of the hyperglycemia criterion when\nestimating the prevalence of clinical obesity but excluded prevalent T2D cases from prospective analyses to focus only on\nincident, physician-confirmed diagnoses. This approach aligns with the Lancet DE Commission’s framing of hyperglycemia\nas both as diagnostic component and a risk state.\n11. The reviewer expressed concerns about the small sample size, lack of control, and regression to the mean, which might\nhave explained the findings. The authors acknowledge regression to the mean and have added analyses using participants\nwith minimal weight loss as a comparator. However, this does not fully resolve the concern that remission of prediabetes\nmay largely reflect variability in fasting or 2-hr glucose measures, which are known to fluctuate. While the deep\nphenotyping in TULIP is a strength, the limitations of sample size, lack of a true control group, and reliance on single glucose\nmeasurements for remission should be more clearly acknowledged.\n12. The reviewer pointed out the existence of larger, randomized weight loss trials that might be more suitable for studying\nremission than TULIP. The authors highlight the detailed phenotyping in TULIP and argue that many large contemporary\nweight loss trials often lack a true placebo group. However, this does not completely address the concern that bigger\nrandomized trials with structured comparator groups and broader clinical criteria could be better suited to study remission\nwithin the clinical obesity framework. While TULIP offers valuable mechanistic insights, its small sample size (only 150 out\nof 413 recruited individuals were included) and absence of a control group remain significant limitations. Specifically, the\nlack of a comparator arm restricts the ability to distinguish genuine remission effects from regression to the mean, especially\ngiven the variability in fasting and 2-hour glucose measures as previously noted. We recommend the authors acknowledge\nthis limitation in the manuscript.\n13. The authors justify the use of a 3% threshold on the basis that it reflected the mean weight loss in TULIP, with the >3%\ngroup achieving a mean loss of 7.4%. However, this rationale feels somewhat arbitrary, and it would be more compelling to\nalign with the conventional 5% benchmark for clinically meaningful weight loss. At minimum, presenting both 3% and 5%\ncut-offs would address the reviewer’s concern and situate the findings more clearly in the clinical context.\n14. The authors have revised their NHANES analyses to apply race- and ethnicity-specific cut-offs for BMI and\nanthropometric measures, as recommended by the Commission. They report only minor impact on prevalence estimates\nand appropriately justify their use of standard cut-offs in the European cohorts (EPIC-Potsdam, TULIP). I find this response\nadequate.\n15. Additional Comments:\ni) In the Introduction, the authors state that “…Both preclinical and clinical obesity groups may harbor subgroups of\nsubstantially different cardiometabolic risk and different requirements and goals of intervention, but evidence necessary for\nfurther staging and assignment of treatment is unavailable yet.” This phrasing could be misinterpreted to suggest that the\npresent study directly compares treatment responses between preclinical and clinical obesity. Since the TULIP analyses\nfocus on individuals with obesity (BMI ≥30) and remission of clinical obesity, without a clear subgroup comparison of\npreclinical vs clinical obesity, I suggest clarifying this point to avoid reader confusion.\nii) Please ensure that all NHANES cycles are labeled as “2017–2018.” There is an error in the results section that shows\nNHANES 2017-2028.", "reviewer_3": "1. ORIGINALITY AND SIGNIFICANCE:\nWhat I understand as the core of this manuscript is assessing the cardiovascular and metabolic import of the classifications\nof clinical/subclinical obesity described by a Lancet Diabetes and Endocrinology Commission.\n• Firstly the inclusion of several populations (NHANES , EPIC Potsdam) is unnecessarily confusing and not clearly justified.\n• The inclusion of an intervention study does not address the main aim of the study. It really should have been the subject of\nanother manuscript.\nA manuscript is not a grant, so it can not have multiple aims unless they are parallel and closely aligned. The interventional\naspect of this manuscript does not contribute to the message of the first part of the manuscript.\n2. DATA AND METHODOLOGY:\nSince the Lancet Diabetes and Endocrinology commission definition of clinical obesity and pre clinical obesity are being\nutilized, these must be explicitly outlined. This is not done in the methods, nor is it done in the methods section. The\nsentences added to the introduction are much too vague.\n3.CONCLUSIONS :\nWhile this point is reasonable, there is no conclusion possible since the aims of the manuscript are so disparate and non\naligned.\n4.SUGGESTED IMPROVEMENTS:\nThis manuscript needs to be more cohesive, and this is only possible if the focus is either on the metabolic/cardiovascular\nimport of the Lancet Diabetes and Endocrinology commission definitions, or the effect of a weight loss intervention on the\nLancet Diabetes and Endocrinology commission categories.\n5.CLARITY AND CONTEXT:\nThe revision retains the same issue: no central theme . You simply have to chose ONE aim and express it clearly , stick with\none population and have a clear take away message. I am not convinced including all aims in one manuscript resulted in\n“important contributions to the discussion around this novel obesity classification concept”, rather it makes your manuscript\ndifficult to understand, and having no unified message.", "reviewer_4": "1. Although the results show significant implications for clinical practice, the authors did not present suggestions on how to\nbetter improve the current classification based on the robust data here presented; Suggestion - A more detailed exploration of\nthe sensitivity of the results to different definitions of clinical obesity would strengthen the analysis;\nOne further question – why is impaired glucose tolerance not used as a criterion in eTable 6? Since this is an important\ncomponent of dysglycaemia, reflecting different aetiology and hence defining a different cohort vs. that defined by IFG.\nI would also recommend a minor edit in line 6 of the Abstract, with addition of the word ‘metabolic’: “More than 80% of adults\nwith confirmed obesity met the criteria for clinical obesity, a proportion influenced by age, BMI and metabolic criteria used”.\nI agree with the authors comment that, whilst of interest, a systematic comparison of different definitions of clinical obesity\nwould likely expand this manuscript beyond its current scope.\n2. The cross-sectional nature of the data does not allow to draw strong conclusions on the associated\ncardiometabolic risk.\n3. Also, the TULIP study did not have a control group; that must be addressed adequately (I would suggest replacing this\ncohort with a more robust study - greater N and control group);\n4. There was significant heterogeneity on the variables used to classify clinical obesity, which could lead to misinterpretation\nof the results. For instance, I would not use self-reported data in this analysis or, at least, the authors could do some\nsensitivity analysis to address this.\n5. Besides liver fat, pancreas fat might actually be an important predictor of cardiometabolic risk. There are cohorts with\nmeasures of pancreatic fat that could be useful to complete this analysis (). This is just a note for thought. I understand that\npractically, liver fat is easier to assess from a clinical perspective.\n6. Suggestion: Explicitly state which potential confounding variables were considered and adjusted for in the analyses (in\nthe methods section).\nI have also added minor notes to the manuscript, that can be found in the attached document.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Response to Reviewers:\n\nReviewer #1:\nRevision Response: Thank you, no changes were made.\nRevision Response: Thank you, no changes were made.\nRevision Response: Thank you, no changes were made.\nRevision Response: Thank you, no changes were made.\nRevision Response: Thank you, no changes were made.\nRevision Response: Thank you, no changes were made.\nRevision Response: We agree that investigating incident HF in preclinical obesity (thus, transition to clinical obesity based on a single criterion) and also among those with clinical obesity but without heart failure at baseline would be interesting additional research questions. However, while we do have detailed biomarker profiles measured in the CVD and T2D case-cohorts, this is unfortunately not the case for incident HF cases. As a consequence, relevant information for the clinical obesity classification, such as HbA1c and blood glucose measurements, are not available for incident HF cases in the EPIC-Potsdam cohort. Omitting these parameters of the criteria would induce misclassification and reduce comparability with the other analyses. We therefore did not perform additional analyses but highlighted this point in the limitations section (p13-14).\nRevision Response: Thank you, no changes were made.\nRevision Response: Thank you for supporting this point, we didn’t make any additional changes.\nRevision Response: Thank you, no changes were made.\nRevision Response: We agree and clearly state those points now in the limitation section (p13-14).\nRevision Response: We agree and clearly state those points in the limitation section now (p13-14).\nRevision Response: We now performed sensitivity analyses using 5% weight-loss cut-offs for comparison and added them to the Supplement (Supplementary Tables 11-14) with a short summary in the results section. The results show comparable results as when using 3% weight loss as the cut-off. However, the small size of the >5% weight-loss subgroup (n= 46) limits the conclusions that can be drawn.\nRevision Response: Thank you, no changes were made.\nRevision Response: We slightly adapted this sentence exclusively referring to clinical obesity. It now reads: Clinical obesity groups may harbor subgroups of substantially different cardiometabolic risk and different requirements and goals of intervention, but evidence necessary for further staging and assignment of treatment is unavailable yet.\nRevision Response: Thank you, we corrected the typo.\n\nReviewer #3:\nRevision Response: We disagree with several aspects of this review. The main criticism repeatedly raised is the lack of focus. Integrating closely related aims within a single manuscript is standard practice and allows a coherent, comprehensive assessment of the research question, maintains consistent definitions, and analytic choices across datasets, and avoids splitting connected results across multiple papers. Combining complementary study designs enables methodological triangulation: NHANES cross-sectional analyses provide nationally representative U.S. prevalence estimates, but incidence information is largely unavailable. Prospective analyses in EPIC-Potsdam address this by estimating incident disease risks and establishing temporality. The TULIP intervention study evaluates responsiveness to preventive treatment and potential remission, thereby, despite its limitations, complementing the observational evidence. Taken together, combining those designs provide more meaningful insights into the potential impact of the newly proposed definition than any single design alone.\n\nReviewer #4:\nRESPONSE: We have included sensitivity analyses for the metabolism criterion of the clinical obesity definition. We used five alternative definitions to compare the proportion of individuals fulfilling the clinical obesity criteria and found relevant differences in the proportional assignment (eTable 6). We have also previously investigated the impact of modifying the criteria (metabolism component) on risk associations (eTable 7). However, our main focus is to evaluate the classification as close as possible to the laid-out criteria – not to provide an alternative classification. We hope the Reviewer agrees that a systematic comparison of alternative definitions across all components included in the definition and their quantification with cardiometabolic risk would go far beyond the scope of this work.\nRevision Response: We exchanged ‘Original definition’ for ‘Metabolism criterion defined as hyperglycemia (self-reported diabetes, impaired fasting glucose or high HbA1c), low HDL-cholesterol levels, and high triglyceride levels’ to be more specific and improve clarity.\nRevision Response: Specific information on impaired glucose tolerance is unfortunately unavailable in NHANES 2017-2018 and EPIC-Potsdam. We support the point, that IGT is a relevant component of dysglycaemia that may identify at-risk individuals not identified by elevated HbA1c or fasting glucose measurements. We have therefore added this point to the limitations section (p. 13-14). Important to consider in this context is that OGTTs are in most countries not among the routine measurements conducted in clinical practice. Therefore, use of HbA1c and IFG as diagnostic measures may reasonably reflect the actual proportion of individuals that would be classified to be clinically obese in practice.\nRevision Response: We agree with this point, but we have removed this section to comply with the journal’s abstract word limit.\nRESPONSE: Analyses in NHANES are cross-sectional, the analyses in EPIC-Potsdam and TULIP are, however, prospective. The analyses in EPIC-Potsdam encompass a median follow-time of ~8 years for CVD and ~6 years for T2D and the follow-up procedures are described in the Methods section. We assume that this was a misunderstanding.\nRevision Response: We now shortened the abstract according to the journal guidelines to 150 words but indicated the prospective nature of the analyses by wording, such as “incidence of cardiovascular diseases and type 2 diabetes”.\nRESPONSE: We used prospective data from the longitudinal EPIC-Potsdam cohort study, which includes 27,548 participants. The embedded case-cohorts were used to estimate cardiometabolic risk associations as described in detail in the Methods section. If referring to TULIP as intervention study, we agree that data from larger weight loss trials could be analyzed. However, we have focused on a study were precise baseline phenotyping was performed in an obese population that underwent a structured lifestyle intervention with repeated measurements of these phenotypes. This allowed us to carefully characterize the participants regarding body fat mass and distribution, liver fat content, insulin sensitivity and insulin secretion, parameters that are closely related to the definition of clinical obesity. To increase the sample size, we now performed the analyses in the whole TULIP study population, including participants who lost ≤3% of body weight. We agree with the reviewer that, thereby, we could provide a broader picture about the changes of parameters defining clinical obesity during a structured lifestyle intervention.\nRevision Response: We are sorry that our description of TULIP in the manuscript confused the reviewer in light of previous publications. Please note that participants were included in the study when they fulfilled at least one of the following criteria: a family history of type 2 diabetes; BMI > 27 kg/m2; IGT (blood glucose ≥ 7.78 mmol/l); or previous diagnosis of gestational diabetes as we describe in the Methods section. Thus, obesity was not a requirement for inclusion. We restricted the analyses for this manuscript to those individuals with BMI≥30 and who underwent the 9-month intervention, with 150 participants meeting these criteria. While this has been described in the Methods section before, we have now rearranged this section (p. 19) to make the flow of participants easier for readers to follow.\nRevision Response: We now clearly acknowledge the limitations of the TULIP study (p.13-14), including the small sample size, lack of a true control group, and the reliance on single glucose and 2h OGTT measurements for remission classification, in the limitations section of the discussion. We also state that a larger intervention study, addressing these issues, is needed to confirm the findings.\nRESPONSE: We did not include a control arm in the TULIP study because we specifically focused on determinants of a successful lifestyle intervention in people at risk of type 2 diabetes. Thus, each participant received the same amount of counselling and motivation to lose weight, which allowed us to study the individual metabolic response to the intervention and the parameters determining this response. In practice, contemporary obesity trials would also unlikely include one arm being pure “no treatment” or “pure placebo” with zero lifestyle or behavioral support. TULIP also did not require specific high-risk states to be present, e.g. prediabetes. Thus, participants reflect a broader spectrum of individuals with obesity. We have used TULIP as data source quite extensively before to investigate treatment response patterns in the context of metabolic phenotypes (e.g. PMIDs: 19889804, 20841609, 21174075, 20042761, 27185609, 28919065 28840257). Still, we now include data also from those participants with smaller or no weight loss (see eTables 8 and 9).\nRevision Response: We accept that and included a statement on the limitations of the TULIP study in the discussion, also highlighting the need for larger intervention studies for confirmation.\nRevision Response: Thank you, no changes were made.\nRevision Response: We now highlight the relevance of investigating pancreatic fat as a potential complementary marker in future studies in the discussion section (p 12).\nRESPONSE: We have added information on the adjustment variables for the TULIP study in the Methods section. The adjustment variables for the prospective analyses in EPIC-Potsdam are already listed there. Prevalence analyses in NHANES did not include adjustments as confounding bias is irrelevant in this context. Since the attached document was not forwarded, the additional notes cannot be addressed at this stage.\nRevision Response: Thank you, no changes were made.\n\nReviewer #5:\nRevision Response: Thank you, we found your review very clear and constructive."}, {"round": 2, "reviewer_comments": {"reviewer_1": "No further comments", "reviewer_4": "Thank you to the authors for the revisions made to the manuscript, and the point by point responses to my questions/suggestions. I believe that it would be informative if the authors choose to look at larger intervention studies in the future, but no further changes to this manuscript suggested.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter