Macronutrient Intake and Dietary Patterns in Indian Adults with and without Type 2 Diabetes: Findings From the Multicentric I-STARCH-1 Study

Macronutrient Intake and Dietary Patterns in Indian Adults with and without Type 2 Diabetes: Findings From the Multicentric I-STARCH-1 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 Macronutrient Intake and Dietary Patterns in Indian Adults with and without Type 2 Diabetes: Findings From the Multicentric I-STARCH-1 Study View ORCID Profile Nitin Kapoor , View ORCID Profile Sanjay Kalra , View ORCID Profile Neeta Deshpande , View ORCID Profile Sheryl S. Salis , View ORCID Profile Smriti Gadia , View ORCID Profile Thamburaj Anthuvan doi: https://doi.org/10.1101/2025.11.04.25339356 Nitin Kapoor 1 Department of Endocrinology, Diabetes and Metabolism, Christian Medical College , Vellore, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nitin Kapoor Sanjay Kalra 2 Consultant Endocrinologist , Bharti Hospital, Karnal, Haryana, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sanjay Kalra For correspondence: brideknl{at}gmail.com Neeta Deshpande 3 Diabetes and Endocrine Centre , Belagavi, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Neeta Deshpande Sheryl S. Salis 4 Nurture Health Solutions Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sheryl S. Salis Smriti Gadia 5 Scientific Services, USV Pvt. Ltd. , Mumbai, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Smriti Gadia Thamburaj Anthuvan 6 Savitribhai Phule Pune University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thamburaj Anthuvan Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract The I-STARCH (Indian Study to Assess Real-World CarboHydrate Consumption) investigation quantified decadal changes in macronutrient intake and carbohydrate quality among Indian adults by benchmarking a 2025 multicentric cross-sectional survey against STARCH 2014. A total of 1,104 adults were enrolled from twenty-nine healthcare centres across fourteen Indian states. Each participant completed three non-consecutive 24 h dietary recalls, harmonised using the ICMR–NIN Food Composition Tables. Temporal contrasts treated 2014 cohort means as fixed benchmarks. In 2025, carbohydrates contributed 62.1% of total energy (2014: 65.8%), fat 25.1% (2014: 21.4%), and protein 12.8% (2014: 12.8%). Carbohydrate quality declined, with simple sugars comprising 20.3% of total carbohydrate (2014: 10.5%) and fibre providing only 1.8% of energy. Across all geographic zones including north, south, east, and west, macronutrient patterns remained suboptimal, indicating persistent dietary imbalance regardless of regional cereal dominance. Adults with type 2 diabetes reported lower carbohydrate intake (61.0%E) than those without diabetes (64.1%E), although both exceeded the recommended 50–55%E range. The composite Diet-Quality Index (protein%E + fibre%E − simple carbohydrate%E; higher = better) was lower in 2025, reflecting refined carbohydrate substitution without improvements in fibre or protein density. A secondary Protein–Fat Quality Index (PFQI = Protein % E ÷ Fat % E) was also computed to assess balance between protein and fat energy. These findings indicate a continuing nutrition transition in India, characterised by persistent carbohydrate predominance, higher dietary fat, and declining carbohydrate quality. Region-specific dietary policies and clinical practices that promote fibre and protein-rich foods, complex carbohydrate sources, and balanced macronutrient quality are needed to support better metabolic health in India. Introduction India’s ongoing nutrition transition is characterised by a persistently high carbohydrate share and concerns about carbohydrate quality. Type 2 diabetes mellitus (T2DM) is a major global health challenge, projected to affect 783 million adults by 2045 (International Diabetes Federation, 2025)( 1 ). India bears a disproportionate burden, with an estimated 101 million people living with diabetes and 136 million with prediabetes in 2023 ( 2 ). Dietary composition is central to both prevention and management of diabetes ( 3 , 4 , 5 ). Diets higher in whole grains, legumes, fruits, vegetables and lean protein sources such as dairy, eggs or fish are associated with better glycaemic outcomes ( 6 , 7 ), whereas those dominated by refined carbohydrates and unhealthy fats are linked to insulin resistance and metabolic dysfunction ( 8 , 9 ). Conventional Indian diets frequently derive 50–70 % of total energy from cereal-based carbohydrates such as white rice and wheat ( 10 , 11 ), and national dietary assessments consistently report inadequate intake of protective foods together with excess salt, sugar and fat ( 12 ). The multicentric STARCH survey (2014) provided an important baseline, reporting that carbohydrates contributed about 64 % of total energy among Indian adults with and without diabetes,well above moderation ranges suggested by more recent Indian analyses linking carbohydrate shares above 56 % of energy with higher metabolic risk ( 11 , 13 ). Since 2014, rapid socioeconomic and food-environment changes, including wider availability of ultra-processed foods, expansion of online food delivery, higher costs of nutrient-dense items and increasingly sedentary lifestyles, have plausibly altered dietary intake ( 8 , 9 , 12 , 14 ). Despite these transitions, contemporary, nationally harmonised, recall-based evidence on macronutrient intake and carbohydrate quality remains scarce. Throughout this paper, macronutrient intakes are expressed as a percentage of total energy (% E). Carbohydrate quality is summarised using a composite Diet-Quality Index (DQI) defined a priori as protein % E + fibre % E − simple carbohydrate % E , with higher values indicating better quality. The Indian Study to Assess Real-World CarboHydrate Consumption (I-STARCH-1) was designed as a follow-up to STARCH 2014, with its protocol published recently ( 15 ). Using harmonised 24 h dietary recalls mapped to ICMR–NIN Food Composition Tables across 29 centres in 14 states, the study provides an updated national view of macronutrient intake and carbohydrate quality among adults with and without T2DM. This investigation quantified decadal changes in macronutrient intake and carbohydrate quality by benchmarking I-STARCH 2025 against STARCH 2014, and further compared dietary patterns by diabetes status and geographic zones to characterise India’s evolving dietary transition. Methodology Study design and setting The Indian Study to Assess Real-World CarboHydrate Consumption (I-STARCH-1) was a multicentric, cross-sectional observational study conducted during the calendar year 2025 across twenty-nine of thirty-three planned healthcare centres in fourteen Indian states. The study was designed to characterise habitual dietary intake and its association with metabolic health among adults with and without type 2 diabetes mellitus (T2DM) across diverse geographical and socioeconomic strata, without modifying participants’ usual behaviours. The methodological approach followed the STROBE checklist for observational studies ( 16 ). Centres represented all four major geographic zones (north, south, east and west) with an urban–rural mix that reflected regional population distribution. Each site operated under uniform protocols for recruitment, dietary assessment, and data quality assurance coordinated through a central monitoring unit. Figure 1 summarises the overall workflow, from site preparation and training to participant enrolment, data collection, and analysis. The final analytic sample comprised 1,104 adults (690 with T2DM; 414 without T2DM). Download figure Open in new tab Figure 1. Study workflow and participant flow in the I-STARCH Study Abbreviations: T2DM, type 2 diabetes mellitus; CRF, case-report form; HbA1c, glycated haemoglobin; FBG, fasting blood glucose; PPBG, post-prandial blood glucose; CHO, carbohydrate; % E, percentage of energy; DQI, Diet-Quality Index . Participants and eligibility Adults aged ≥ 18 years with or without type 2 diabetes mellitus (T2DM) were recruited from outpatient clinics, hospitals, and community programmes across fourteen Indian states. T2DM was defined by a documented diagnosis or by self-report with current glucose-lowering medication. Exclusion criteria were pregnancy, lactation, acute or chronic illnesses that substantially alter habitual diet (for example, malignancy, severe hepatic or renal disease, or eating disorders), participation in weight-loss programs, or inability to complete dietary recalls. All participants provided written informed consent and recent (≤ 3 months) medical records. Screening and enrolment followed a standardised flow, and the analytic cohort included individuals with complete dietary and outcome data. Eligibility criteria are summarised in Table 1 View this table: View inline View popup Download powerpoint Table 1. Eligibility criteria for study participants Recruitment and sample size Each participant completed three non-consecutive 24 h dietary recalls scheduled to include two weekdays and one weekend day. Trained dietitians conducted interviews using a standardised multi-pass script with calibrated utensil sets, portion-size photographs, and probe lists to record brand, recipe, preparation method, and context of intake. Field supervisors reviewed each recall for completeness, performed random verification calls, and resolved discrepancies through direct participant confirmation. Data were entered into a unified electronic template with locked numeric fields, automated plausibility ranges, and time-stamped audit trails. Implausible total energy intakes ( 4500 kcal d⁻¹) or macronutrient totals outside 98–102 % of energy were flagged for re-check. Records failing verification were excluded. This multi-step process ensured internal consistency and comparability of dietary data across all study sites. Food composition and harmonisation All reported food items were mapped to the ICMR–NIN Food Composition Tables (2020) using a prespecified codebook to ensure uniform nutrient attribution across centres. Mixed dishes were decomposed into ingredients based on standard or locally validated recipes, and brand-specific products were matched to equivalent generic codes when composition data were unavailable. Total energy (kcal d⁻¹) and macronutrients (carbohydrate, protein, fat) were computed using Atwater factors and expressed both as absolute intakes and as percentage of total energy (% E). Simple carbohydrate was defined as mono- and disaccharides from intrinsic and added sugars identified at the food-code level, while fibre represented total dietary fibre as per ICMR–NIN definitions. Derived variables included simple- and complex-carbohydrate shares and composite indices of diet quality. These procedures ensured consistency, comparability, and traceability of nutrient data across all participating sites. Plausibility screening and data quality Daily energy intakes were screened for plausibility using Goldberg cut-offs, with basal metabolic rate estimated from age, sex and body mass. Recalls showing implausible total energy or macronutrient distributions, unresolved portion-size discrepancies, or missing critical fields were flagged for supervisor review and participant re-contact where feasible. Analyses used the cleaned dataset after all quality-control steps. Interviewer training was standardised across centres and included calibration exercises, with periodic monitoring by the coordinating site to ensure procedural uniformity. Anthropometry and clinical data Height, weight and waist circumference were measured using calibrated equipment and standardised protocols. Body-mass index (BMI) was calculated as weight (kg) / height (m)². Where available, fasting plasma glucose, glycated haemoglobin (HbA1c) and lipid profiles were abstracted from verified medical records within a ± 3-month window of the dietary assessment. Use of glucose-lowering and lipid-modifying medication was recorded from prescriptions or participant-verified lists. Regional classification and strata Participating centres were grouped into four predefined geographic zones—north, south, east and west—according to state location. Primary analyses summarised national means, while zone-wise estimates and stratified comparisons were performed by diabetes status (T2DM v. non-T2DM) to assess regional heterogeneity and glycaemic-state contrasts. Diet-quality indicators Two complementary indices were prespecified. Diet-Quality Index (DQI): defined as protein % E + fibre % E − simple carbohydrate % E; higher values indicate better dietary quality. Protein–Fat Quality Index (PFQI): defined as protein % E ÷ fat % E; lower values indicate a relative shift towards fat energy over protein. Both indices were computed at the participant level using nutrient values averaged across the three 24 h recalls. Outcomes Primary outcomes were macronutrient distribution (percentage of total energy [% E] from carbohydrate, protein and fat) and DQI. Secondary outcomes included simple carbohydrate as a proportion of total carbohydrate, fibre density (g / 1000 kcal) and PFQI. Prespecified subgroup analyses contrasted participants with and without T2DM and compared geographic zones. Benchmarking and statistical analysis Temporal comparisons benchmarked I-STARCH 2025 against STARCH 2014 cohort means. For DQI, 2014 values for simple carbohydrate and fibre were drawn from the published STARCH report, while protein % E was taken from the 2014 mean where available or anchored to the 2025 mean when equivalent resolution was lacking. The 2014 DQI standard deviation was approximated by variance summation under independence to provide a conservative reference. Continuous variables are reported as mean ± standard deviation (SD) with 95 % confidence intervals (CI); categorical variables as n (%). Within-2025 comparisons across diabetes status and geographic zones used independent t-tests or one-way ANOVA as appropriate; proportions used χ² tests. Decadal contrasts treated 2014 cohort means as fixed benchmarks and applied two-sided one-sample t-tests to 2025 participant-level data (α = 0·05). Sensitivity analyses with robust (Huber–White) standard errors and multiple imputation were performed when missingness exceeded 5 %. Statistical analyses were conducted using IBM SPSS Statistics version 29·0 (IBM Corp., Armonk, NY, USA). Statistical significance was defined as P < 0·05. Ethics and governance The study adhered to the Declaration of Helsinki, the Indian Council of Medical Research (ICMR) guidelines and Good Clinical Practice standards. Central and site-specific ethics approvals were obtained before participant enrolment; approval identifiers are listed in Supplementary Table S1. Written informed consent was obtained from all participants. Data were de-identified and stored on secure, password-protected servers with restricted access. The peer-reviewed protocol is available in Kalra et al. (2025)( 15 ). As this was an observational study, formal trial registration was not applicable. Results Participant characteristics A total of 1104 adults from fourteen Indian states were enrolled, with balanced gender representation (49 % male, 51 % female) and broad geographic coverage across the north, south, east and west zones ( Table 2 ). Of these, 690 (62·5 %) had T2DM and 414 (37·5 %) did not. Participants with T2DM were older on average and more likely to report dietary restriction during the past year (P < 0·001). Geographic distribution differed marginally between groups (P = 0·055). Gender distribution, regular exercise, alcohol use, meal frequency and water intake did not differ significantly by diabetes status. These baseline characteristics provide the context for subsequent dietary analyses. View this table: View inline View popup Table 2. Baseline Demographic and Lifestyle Characteristics (N = 1,104) Energy and Macronutrient Intake Patterns Mean daily energy intake in 2025 was 1 788 ± 565 kcal day⁻¹. Carbohydrates contributed 62.1 % of total energy, followed by fat (25.1 %) and protein (12.8 %) ( Table 3 ; Figure 2 , panel A). Dietary fibre provided 1.8 % of total energy. Simple carbohydrates accounted for 12.5 % of total energy, representing 20.3 % of total carbohydrate intake, whereas complex carbohydrates comprised 79.7 % ( Figure 2 , panel B). Download figure Open in new tab Figure 2. Macronutrient distribution and carbohydrate quality among Indian adults with and without type 2 diabetes Panel A: Percentage contribution of carbohydrate, protein and fat to total daily energy intake. Panel B: Proportion of complex and simple carbohydrates within total carbohydrate intake. Values are means ± SD. Abbreviations: CHO, carbohydrate; %E, percentage of total energy; T2DM, type 2 diabetes mellitus. View this table: View inline View popup Download powerpoint Table 3. Macronutrient profile by diabetes status in the I-STARCH 2025 cohort Diabetes status contrasts Across the cohort, both participants with and without type 2 diabetes mellitus (T2DM) derived most of their total energy from carbohydrates ( Table 3 ; Figure 3 , panel A). Mean carbohydrate contribution exceeded 60 % of total energy in both groups. Participants with T2DM consumed a lower proportion of energy from carbohydrates and a higher proportion from fat and protein than those without T2DM (carbohydrate 61.0 ± 5.4 % v. 64.1 ± 5.0 %; fat 25.9 ± 5.5 % v. 23.7 ± 5.3 %; protein 13.1 ± 3.6 % v. 12.2 ± 3.3 %; all P < 0.001). Total energy intake was lower among individuals with T2DM (1 616 ± 280 kcal day⁻¹) than among those without T2DM (2 076 ± 766 kcal day⁻¹; P < 0.001). Download figure Open in new tab Figure 3. Dietary carbohydrate quality and fibre density by diabetes status. Panel A: Percentage contribution of carbohydrate, fat, and protein to total daily energy intake among adults with and without T2DM. Panel B: Mean simple-carbohydrate proportion (as % of total CHO) and dietary fibre density (as % of total energy) across diabetes strata. Values are means ± SD with 95 % confidence intervals. Abbreviations: CHO, carbohydrate; %E, percentage of total energy; CI, confidence interval; T2DM, type 2 diabetes mellitus . Carbohydrate quality was suboptimal in both strata ( Figure 3 , panel B). Simple carbohydrates accounted for a substantial proportion of total carbohydrate and were higher among those without T2DM (22.8 ± 6.6 %) than in those with T2DM (18.5 ± 5.8 %; P < 0.001). Fibre contributed less than 2 %E on average in both groups, with similar absolute intakes of about 15 g day⁻¹ (T2DM 15.5 ± 3.2 v. non-T2DM 15.2 ± 3.2 g day⁻¹; P = 0.07). The imbalance toward simple carbohydrates was evident across both strata, consistent with national dietary patterns. Regional variation in dietary patterns Macronutrient distribution was broadly similar across India’s four geographic zones — east, west, north, and south — indicating national convergence ( Table 4 ; Figure 4 , panel A). Carbohydrates dominated energy intake in all zones, contributing about 62 %E (range 61.5–63.2 %E), with fat contributing 23–27 %E and protein generally below 14 %E. A modest east-to-south gradient was observed, with slightly lower carbohydrate %E and correspondingly higher fat %E in the south and north compared with the east; these differences were small and not statistically significant (P > 0.05). Download figure Open in new tab Figure 4. Regional variation in macronutrient distribution and carbohydrate quality in Indian adults Panel A: Percentage contribution of carbohydrate, protein, and fat to total daily energy intake by geographic zone. Panel B: Mean carbohydrate %E and simple-CHO proportion (as % of total CHO) across zones. Values are means ± SD; error bars represent 95 % confidence intervals. Abbreviations: CHO, carbohydrate; %E, percentage of total energy; CI, confidence interval; T2DM, type 2 diabetes mellitus . View this table: View inline View popup Download powerpoint Table 4. Zone-wise macronutrient distribution and carbohydrate quality in the I-STARCH 2025 cohort Carbohydrate quality showed minimal zonal divergence ( Figure 4 , panel B). Simple sugars comprised roughly one-fifth of total carbohydrate (≈ 19.6–21.5 %), and fibre density was uniformly low (1.7–1.9 %E). Within-zone contrasts by diabetes status were negligible in the east. In the north, south, and west, participants with T2DM consumed slightly less carbohydrate %E and more fat %E than those without T2DM; in the west, protein %E was also marginally higher among individuals with T2DM. These regional trends parallel the national pattern of high carbohydrate reliance with modest macronutrient redistribution among adults with T2DM. Temporal Dietary Trends: STARCH 2014 vs I-STARCH 2025 Between 2014 and 2025, national macronutrient profiles shifted towards lower carbohydrate and higher fat contributions to total energy, while protein %E remained broadly unchanged ( Table 5 ). Mean daily energy intake was modestly lower in 2025. Carbohydrate quality declined over the decade: the share of simple carbohydrate increased markedly, and fibre %E showed a slight absolute reduction. The direction and magnitude of these changes were consistent across diabetes strata. View this table: View inline View popup Download powerpoint Table 5. Comparison of macronutrient distribution between STARCH 2014 and I-STARCH 2025 Temporal change in carbohydrate quality (2014 v. 2025) Carbohydrate composition shifted decisively towards greater refinement between 2014 and 2025, marked by a substantial rise in simple carbohydrates and a corresponding fall in complex carbohydrates as a proportion of total carbohydrate ( Table 6 ). These changes were statistically significant both overall and within diabetes strata, underscoring a persistent national dependence on carbohydrate energy coupled with declining carbohydrate quality. Sensitivity analyses adjusting for age and sex produced consistent results for fat %E and simple-carbohydrate share. View this table: View inline View popup Download powerpoint Table 6. Comparison of simple and complex carbohydrate intake (% of total carbohydrate) between STARCH 2014 and I-STARCH 2025 Dietary Quality Indicators Composite Diet-Quality Index (DQI) Despite a modest decline in carbohydrate %E over the decade (Section 3.5), overall dietary quality worsened ( Table 7 ). Simple carbohydrate increased substantially, both as a share of total energy and within total carbohydrate, whereas protein %E and fibre %E remained largely unchanged. The composite DQI—calculated as protein %E + fibre %E − simple CHO %E (higher scores indicating better diet quality)—was significantly lower in 2025 than in 2014. This decline reflects a shift towards refined carbohydrates without compensatory gains in protein or fibre, signifying a qualitative deterioration in macronutrient composition. View this table: View inline View popup Download powerpoint Table 7. Temporal comparison of carbohydrate-quality metrics and composite DQI (2014 vs 2025) Protein–Fat Quality Index (PFQI) across cohorts and glycemic status Across the decade, the Protein–Fat Quality Index (PFQI = protein %E ÷ fat %E) declined in the I-STARCH 2025 cohort compared with the STARCH 2014 reference ( Table 8 ), indicating a relative shift towards higher fat and lower protein density. The reduction was consistent across diabetes strata, with similar decreases among participants with and without T2DM; all contrasts were statistically significant. View this table: View inline View popup Download powerpoint Table 8. Temporal comparison of Protein–Fat Quality Index Discussion Principal Findings National macronutrient profiles indicated a decade-scale shift towards lower carbohydrate %E and higher fat %E, with protein %E remaining broadly stable ( Table 5 ). Carbohydrate quality, however, deteriorated: the proportion of simple carbohydrates increased and fibre %E declined modestly, yielding significantly lower composite Diet-Quality Index (DQI = Protein %E + Fibre %E − Simple CHO %E) scores in 2025 compared with the 2014 benchmark ( Table 7 ). The Protein–Fat Quality Index (PFQI = Protein %E ÷ Fat %E) also decreased, reflecting greater reliance on fat energy relative to protein ( Table 8 ). These patterns were consistent across diabetes strata.Participants with type 2 diabetes reported slightly lower carbohydrate %E and marginally better carbohydrate quality than those without and broadly concordant across geographic zones. Despite small absolute changes in macronutrient shares, the overall pattern represents qualitative deterioration driven by refined-carbohydrate displacement without compensatory gains in protein or fibre. Collectively, these findings suggest that India’s dietary “modernisation” has been quantitative rather than nutritional, a trajectory with clear implications for its persistent cardiometabolic risk profile. Comparison with Prior Literature Consistent with India’s ongoing nutrition transition, the present findings align with evidence of modest declines in total carbohydrate %E since the STARCH 2014 baseline ( 11 ) accompanied by worsening carbohydrate quality. National and cohort-based research indicates continued dominance of refined cereals, expanding processed-food penetration, and persistently low intake of fibre and whole grains ( 17 , 18 , 19 , 20 ). These patterns are supported by contemporary Indian cohort analyses showing that unhealthy dietary composition, rather than total energy intake, is the more important determinant of cardiometabolic risk ( 13 , 21 ). Beverage-related free-sugar exposure further reinforces this trajectory, with poorer beverage-quality scores associated with greater metabolic-syndrome risk among South-Asian adults ( 22 ). In contrast, large European and United Kingdom cohorts have reported stable or improving fibre density and declining added-sugar consumption in several subgroups, suggesting gradual qualitative improvement in carbohydrate intake ( 23 ). Diverging from this global trend, Indian diets remain characterised by low fibre density and increasing dependence on simple carbohydrates, consistent with the lower Diet-Quality Index and Protein–Fat Quality Index observed in this study. Together, these data place the present findings within a broader international context, indicating that India’s dietary transition has progressed quantitatively but not qualitatively, thereby sustaining a high burden of cardiometabolic risk. Mechanistic and Behavioural Interpretations The physiological consequences of refined-carbohydrate dominance are well established and provide a coherent explanation for the decade-scale patterns observed. Diets characterised by high-glycaemic-load staples and added sugars provoke exaggerated post-prandial glycaemia and hyperinsulinaemia, promoting hepatic de novo lipogenesis, ectopic fat deposition and atherogenic dyslipidaemia while progressively impairing insulin sensitivity ( 24 , 25 , 26 ). In parallel, chronically low intake of fermentable fibre limits short-chain-fatty-acid production by the gut microbiota, weakening satiety signalling via GLP-1 and PYY and reducing colonic and hepatic glucose buffering, thereby amplifying glycaemic variability and insulin resistance ( 27 , 28 ). These physiological effects are increasingly documented in South-Asian populations exposed to rapidly modernising food systems. Behavioural and environmental drivers further magnify these risks: urbanisation, digital food-delivery access and convenience-oriented retail have expanded the availability of refined cereals, processed snacks and sugar-sweetened beverages, while affordability constraints limit substitution towards pulses, whole grains and higher-quality proteins ( 29 , 30 ). Together, these interconnected metabolic and behavioural pathways explain how declining carbohydrate quality,rather than energy excess alone continues to underpin India’s growing cardiometabolic burden ( 21 , 24 ). Public-health implications Translating these findings into policy requires a shift from total-carbohydrate restriction towards improving carbohydrate quality, fibre and protein density and overall macronutrient balance. Fibre density, expressed as grams per 1000 kcal, should be embedded across surveillance, procurement and programme-evaluation frameworks, consistent with national recommendations ( 17 , 31 ). Moving beyond calorie sufficiency, staple quality can be enhanced by integrating whole graincereals, pulses and millets within the Public Distribution System and school-meal programmes, drawing on evidence that millet-based substitutions improve micronutrient adequacy, child-nutrition outcomes and community acceptability ( 13 , 32 ). Procurement norms that specify minimum fibre and protein density, define protein-to-fat ratio targets and cap free-sugar content in publicly purchased foods, supported by front-of-pack labelling and fiscal disincentives for high-sugar staples and beverages—would reinforce these aims while aligning with Eat Right India and the Millet Mission. Such actions are consistent with expert consensus emphasising fibre-rich diets and ethical, health-focused marketing ( 33 , 34 ). With cross-sector coordination under the NPCDCS framework, these measures could shift institutions towards quality-first nutrition, prevent unintended high-fat substitution and stimulate sustained demand for higher-fibre, higher-protein and lower-sugar staples ( 17 , 31 ). Clinical translation Within clinical pathways, the same principle applies: embed diet-quality counselling into routine metabolic and diabetes care to enable culturally compatible food substitutions, replacing refined rice and wheat with whole-grain or unpolished millet varieties, substituting sugar-sweetened beverages with unsweetened or fermented milk drinks and limiting the frequency of sweets. Interventions prioritising reductions in refined-carbohydrate and beverage-sugar exposure over total-energy restriction have improved glycaemic stability and lipid profiles in Indian and other Asian populations ( 21 , 22 ). The Endocrine Society of India’s guidance on obesity management similarly underscores coupling dietary-quality improvements with pharmacological and behavioural strategies, reflecting the nation’s high obesity prevalence amid poor protein and fibre intake ( 35 ). Scaling structured counselling through NPCDCS and Eat Right India can harmonise procurement standards with clinical and community education, embedding fibre density, free-sugar share and protein-to-fat ratio as measurable indicators. Comparative evaluations across programmes should also account for age structure and common dietary restrictions that influence achievable macronutrient targets and feasible substitutions. Collectively, improving carbohydrate quality and maintaining balanced macronutrient ratios represent practical, equitable and scalable strategies to reduce cardiometabolic risk while advancing Sustainable Development Goals 2 (Zero Hunger), 3 (Good Health and Well-Being) and 12 (Responsible Consumption and Production) ( 17 , 31 ). Relevance in the era of pharmacologic weight loss The present findings assume added significance amid the expanding use of anti-obesity pharmacotherapies, particularly GLP-1 receptor agonists and dual GIP/GLP-1 agonists, now increasingly prescribed in India for diabetes and obesity management. Although these agents achieve substantial reductions in total and fat mass, around 15–25 % of weight loss typically arises from lean tissue, raising concern for sarcopenia and functional decline in populations with low baseline protein intake ( 36 , 37 , 38 , 39 ). Comparative data suggest liraglutide may cause slightly less lean-mass loss than semaglutide or tirzepatide, though the overall direction of effect is consistent across agents ( 37 ). These observations highlight the need to couple pharmacologic weight loss with adequate dietary protein and progressive resistance exercise to preserve muscle mass and metabolic resilience ( 38 ). Given the I-STARCH evidence of low protein intake and declining carbohydrate quality, the risk of sarcopenic obesity may increase as these medications become more accessible. Culturally aligned behavioural approaches can complement pharmacotherapy; for instance, bite-count reduction has produced sustainable weight loss with lean-mass preservation in Indian adults ( 40 ). Clinicians should therefore integrate structured nutrition counselling that emphasises high-quality protein sources such as dairy, fish, lean meat, eggs and pulses,alongside resistance training and regular assessment of body composition, waist circumference, waist to height ratio rather than body weight alone. Aligning dietary-quality optimisation with pharmacotherapy can mitigate lean-mass depletion, maintain metabolic health and ensure that the benefits of obesity treatment remain durable and safe within the Indian nutritional context. Strengths and Limitations This study draws on a large, multicentric contemporary sample with a decade-spaced comparator, enabling robust assessment of temporal dietary change across diverse Indian settings. Consistent definitions of simple and complex carbohydrates across both datasets enhance internal comparability, while convergent indicators synthesised into a composite Diet-Quality Index (DQI) provide an interpretable metric for clinical and policy use. The dataset spans regional and metabolic strata, capturing real-world consumption patterns among adults with and without diabetes. Limitations include a partial harmonisation gap for protein data in 2014 that may introduce minor between-period variance, recall-based dietary assessment prone to reporting bias, and limited seasonality representation in single-time-point recalls. The 2025 analysis is cross-sectional and cannot infer causality, and residual confounding by socioeconomic, urbanicity and food-sourcing factors may persist despite stratification. Future Directions Several methodological and translational priorities arise from these findings. First, the Diet-Quality Index (DQI) should be validated against objective biomarkers of glycaemic control, lipid metabolism and hepatic health to establish its predictive value and responsiveness. Second, DQI domains should be linked with food-environment variables, such as public-distribution access, retail diversity, affordability of coarse cereals and pulses relative to refined staples, and delivery-app penetration, to identify environmental determinants of carbohydrate quality. Third, pragmatic field trials in community and clinic settings are needed to test staple and beverage substitutions that raise fibre density and reduce free-sugar exposure, explicitly assessing scalability, adherence and equity. Finally, future studies should standardise reporting of dietary-quality metrics, distinguishing free from added sugars, expressing fibre density per 1 000 kcal, incorporating simple meal-timing indices and adopting harmonised beverage-quality scores to facilitate comparability and policy translation across surveillance, procurement and clinical platforms. Conclusion Across a large, multicentric Indian cohort benchmarked to STARCH 2014, total carbohydrate intake declined modestly while fat share rose, yet carbohydrate quality deteriorated, characterised by higher simple-sugar intake, persistently low fibre density and a lower composite Diet-Quality Index in 2025. These trends were consistent across regions and metabolic strata, indicating a qualitative rather than balanced nutrition transition. Convergent evidence from national cohorts and validation studies underscores this pattern: digital dietary-assessment tools reliably rank diet quality at scale, quality declines under clinical stressors such as gestational diabetes, and performance populations demonstrate diet-quality shortfalls that parallel metabolic risk. Together, these data reinforce the need to prioritise quality over quantity, focusing on carbohydrate quality, fibre and protein density and restriction of free sugars. Translation should now pivot to quality-first targets by integrating legumes, whole grains and millets into procurement and safety-net programmes, defining minimum fibre and protein density with caps on added sugars, and scaling clear front-of-pack labelling and fiscal disincentives for high-sugar products. Within clinical care, culturally compatible substitutions such as whole-grain cereals and millets, unsweetened or fermented dairy, and routine nutrition counselling should become standard, complemented by resistance activity as pharmacologic weight-loss therapies expand. Improving carbohydrate quality and fibre density offers a practical and equitable pathway to reduce India’s cardiometabolic burden while advancing national nutrition and sustainability goals. Declarations Participating Investigators and Study Centres The authors gratefully acknowledge the site investigators and their teams across 29 centres for their contributions to study coordination and data collection: Dr Ajish T. P. (Kollam), Dr Anamika Samant (Mumbai), Dr Anwar Jamal (Kolkata), Dr Arun Karthik (Coimbatore), Dr D. K. Srivastava (Patna), Dr G. Shanmugasundar (Chennai), Dr Hamid Ashraf (Aligarh), Dr Hiranmoy Paul (Siliguri), Dr Jagdish Mulchandani (New Delhi), Dr K. Venugopal Reddy (Vijayawada), Dr Kalpesh Kavar (Rajkot), Dr Manash Baruah (Guwahati), Dr Neelakshi Deka (Guwahati), Dr Neeta Deshpande (Belagavi), Dr Nilesh Detroja (Rajkot), Dr Nitin Kapoor (Coimbatore), Dr P. K. Gupta (Chennai), Dr P. Sasikanth Reddy (Hyderabad), Dr R. K. Singh (Samastipur), Dr Raka Sheohore (Raipur), Dr Rupam Choudhary (Guwahati), Dr S. Paramesh (Bengaluru), Dr Saqib Ahmad Khan (Lucknow), Dr Saurabh Arora (Ludhiana), Dr Shaibal Guha (Patna), Dr V. K. Dhandania (Ranchi), Dr Yogesh Varge (Akola), Dr Yogesh Yadav (Dehradun) and Dt. Sheryl S. Salis (Mumbai). Funding This research was supported by USV Private Limited, Mumbai, India. The views expressed are those of the authors and do not necessarily reflect those of the funder. The sponsor had no role in the study design; data collection, management, analysis or interpretation; manuscript writing; or the decision to submit for publication. Role of the funder The sponsor had no role in the study design; data collection, management, analysis, or interpretation; writing of the report; or the decision to submit the manuscript for publication. Conflicts of interest Thamburaj Anthuvan and Smriti Gadia are employees of USV Private Limited. The remaining authors serve as independent scientific advisers to USV Private Limited. All analyses and the decision to publish were undertaken independently of the sponsor. The authors declare no other conflicts of interest. Author contributions Conceptualisation: S.K., T.A. Methodology: S.K., N.K., N.D., S.S.S. Investigation: S.S.S.., S.G. Data curation: T.A., S.S.S. Formal analysis: T.A., S.S.S. Writing—original draft: T.A., S.S.S. Writing—review and editing: S.K., N.K., N.D., S.G. Supervision: S.K. Project administration: T.A., S.S.S. Guarantor: S.K. All authors had access to the aggregated data and approved the final manuscript. Guarantor S.K. accepts full responsibility for the work as a whole, had access to the data, and controlled the decision to submit for publication. Ethical standards disclosure The study was conducted in accordance with the Declaration of Helsinki and the Indian Council of Medical Research (ICMR) National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (2017). Ethical approval for this work was granted by the Shah Lifeline Hospital and Heart Institute Ethics Committee, Mumbai, Maharashtra, India (Approval No. MCR/CT/0424/04). The committee reviewed and approved the study. Written informed consent was obtained from all participants prior to enrolment. The approved study protocol is published as Kalra et al. (2025) in the International Journal of Clinical Metabolism and Diabetes ( https://doi.org/10.1177/30502071251375540 ). Data availability statement De-identified data supporting the findings of this study are available from the corresponding author upon reasonable request for non-commercial academic use, subject to institutional approvals and a data-use agreement. Code availability Custom code used for data harmonisation and analysis is available from the corresponding author upon reasonable request and will be deposited in a public repository upon manuscript acceptance. Patient and public involvement Patients or members of the public were not involved in the design, conduct, reporting or dissemination of this research. Transparency statement The authors affirm that this manuscript is an honest, accurate and transparent account of the study being reported, that no important aspects have been omitted, and that any discrepancies from the planned methods have been explained. 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