Background: The coexistence of type 2 diabetes mellitus (T2DM) and hypertension significantly amplifies cardiovascular risk, yet age-specific prevalence patterns of undiagnosed hypertension in newly diagnosed T2DM patients remain inadequately characterized.

Objective: To investigate the age-dependent prevalence of silent hypertension in adults with newly diagnosed T2DM and evaluate associated cardiovascular risk factors.

Design: Multicentre, cross-sectional observational study.

Setting: Two tertiary diabetes centres in Mumbai and Patiala, India.

Participants: 591 adults with T2DM diagnosed within 3 months, stratified by age: 18-40 years (n=214), 41-50 years (n=164), and ≥50 years (n=213).

Main outcome measures: Prevalence of undiagnosed hypertension (≥140/90 mmHg) across age groups; secondary outcomes included blood pressure parameters, cardiovascular risk factor clustering, and 10-year cardiovascular risk stratification.

Results: Silent hypertension prevalence increased dramatically with age: 11.2% (20/178) in 18-40 years, 23.0% (32/139) in 41-50 years, and 50.0% (93/186) in ≥50 years groups (p<0.001). The mean systolic blood pressure increased progressively across age groups but remained below the hypertension threshold, suggesting subclinical vascular changes. Older adults demonstrated high prevalence of metabolic syndrome (73.2%), dyslipidaemia (66.7%), and obesity (51.1%). Age, BMI, HbA1c, family history of hypertension, and waist circumference were independent predictors of hypertension.

Conclusion: Half of adults aged ≥50 years with newly diagnosed T2DM have undiagnosed hypertension, highlighting a pivotal opportunity for early cardiovascular risk mitigation through systematic screening and multifactorial management.

Keywords: type 2 diabetes mellitus, silent hypertension, cardiovascular risk, age stratification, screening protocols

Type 2 diabetes mellitus (T2DM) is a global health challenge with an estimated 537 million adults affected worldwide, projected to rise to 783 million by 2045.¹ T2DM frequently coexists with hypertension, a combination that markedly elevates cardiovascular risk.^2,3 The landmark UK Prospective Diabetes Study (UKPDS) demonstrated a "legacy effect," where early intensive blood pressure control in type 2 diabetes patients resulted in sustained reductions in macrovascular complications and diabetes-related mortality over the long term, even beyond the active treatment phase.^10–12 This legacy effect underscores the critical importance of early detection and treatment of hypertension at diabetes diagnosis.

Hypertension and diabetes synergistically increase risks of coronary disease, stroke, and mortality by 2-4 folds compared with either condition alone.^4,5 Shared pathophysiology, including insulin resistance, endothelial dysfunction, chronic inflammation, and renin-angiotensin system dysregulation, contributes to this risk.⁶ Despite clinical guidelines advocating cardiovascular risk assessment in T2DM,⁷ data on silent hypertension prevalence stratified by age at the time of diabetes diagnosis remain limited.⁸

Silent hypertension often progresses unnoticed to end-organ damage before detection.⁹ Prior studies have mainly focused on established diabetes cohorts without age stratification.^10,11 Understanding the age-dependent prevalence of silent hypertension and associated cardiometabolic risk factors in newly diagnosed T2DM patients is vital to designing effective screening and intervention strategies. This study addresses this gap through a multicentre cross-sectional analysis in India, spanning a wide adult age range and comprehensive cardiovascular risk profiling.

Study design and ethical approval

This multicenter, cross-sectional observational study was conducted between January 1, 2023, and December 31, 2023, at two tertiary care diabetes centers: the Diabetes and Wellness Clinic, Mumbai, and Omnia Hospital, Patiala, India.

Study population

Inclusion criteria

1. Adults aged ≥18 years
2. Newly diagnosed T2DM within 3 months of enrollment
3. T2DM diagnosis confirmed by American Diabetes Association criteria:¹³ HbA1c ≥6.5% (48 mmol/mol), fasting plasma glucose ≥126 mg/dL (7.0 mmol/L), or 2-hour plasma glucose ≥200 mg/dL (11.1 mmol/L) during 75g oral glucose tolerance test
4. Ability to provide informed consent and participate in study procedures

Exclusion criteria

1. Prior diabetes diagnosis (Type 1 or Type 2)
2. Known hypertension or current antihypertensive medication use
3. Secondary causes of diabetes (pancreatogenic, drug-induced, monogenic)
4. Pregnancy or lactation
5. Acute illness or hospitalization within 30 days
6. End-stage renal disease (eGFR <15 mL/min/1.73m²)
7. Active malignancy or life expectancy <1 year
8. Inability to provide informed consent

Sample size calculation

Sample size was calculated using the formula for cross-sectional studies comparing proportions across multiple groups. Based on pilot data suggesting hypertension prevalence of 15%, 25%, and 45% in the three age groups, with 80% power and α=0.05, the minimum required sample size was 540 participants. To account for potential dropouts and ensure balanced representation across age strata, we aimed to recruit 600 participants.

Age stratification

Participants were stratified into three clinically relevant age groups:

1. Young adults: 18-40 years
2. Middle-aged adults: 41-50 years
3. Older adults: ≥50 years

These age cutoffs were selected based on epidemiological evidence of increasing cardiovascular risk and previous diabetes research frameworks.^14,15

Data collection procedures

Clinical assessment

All participants underwent standardized clinical evaluation by trained healthcare professionals using validated protocols:

Anthropometric measurements

1. Height: Measured to nearest 0.1 cm using wall-mounted stadiometer (Seca 206, Hamburg, Germany)
2. Weight: Measured to nearest 0.1 kg using calibrated digital scale (Tanita BC-418, Tokyo, Japan)
3. Body Mass Index (BMI): Calculated as weight(kg)/height(m)²
4. Waist circumference: Measured at midpoint between lowest rib and iliac crest using non-stretchable tape
5. Hip circumference: Measured at widest point over greater trochanters

Blood pressure measurement: Blood pressure assessment followed American Heart Association guidelines:¹⁶

1. Minimum 5-minute rest period in quiet environment
2. Participant seated with back supported, arm at heart level
3. Appropriate cuff size selection based on arm circumference
4. Automated oscillometric device (Omron HEM-7120, Kyoto, Japan) validated for accuracy
5. Three consecutive measurements at 2-minute intervals
6. Average of second and third readings recorded as final blood pressure

Hypertension definition

1. Systolic BP ≥140 mmHg and/or diastolic BP ≥90 mmHg
2. Grade 1 hypertension: 140-159/90-99 mmHg
3. Grade 2 hypertension: 160-179/100-109 mmHg
4. Grade 3 hypertension: ≥180/≥110 mmHg

Laboratory investigations

Venous blood samples were collected after minimum 8-hour overnight fast for comprehensive metabolic assessment:

Glycaemic parameters

1. Fasting plasma glucose: Glucose oxidase method (Beckman Coulter AU480, Brea, CA)
2. HbA1c: High-performance liquid chromatography (Bio-Rad D-10, Hercules, CA)
3. Glucose management indicator (GMI): Calculated from HbA1c

Lipid profile

1. Total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides: Enzymatic colorimetric assays (Roche Cobas c311, Basel, Switzerland)
2. Non-HDL cholesterol: Calculated as total cholesterol - HDL cholesterol

Renal function

1. Serum creatinine: Jaffe kinetic method
2. Estimated glomerular filtration rate (eGFR): CKD-EPI equation
3. Urinalysis and micro albuminuria assessment

Additional parameters

1. Liver function tests: Alanine aminotransferase (ALT), aspartate aminotransferase (AST)
2. Complete blood count with differential
3. Thyroid-stimulating hormone (TSH)
4. High-sensitivity C-reactive protein (hs-CRP)

Cardiovascular risk assessment

Metabolic syndrome definition: International Diabetes Federation criteria:¹⁷

1. Central obesity plus two or more of: raised triglycerides (≥150 mg/dL), reduced HDL cholesterol (<40 mg/dL in males, <50 mg/dL in females), raised blood pressure (≥130/85 mmHg), raised fasting plasma glucose (≥100 mg/dL)

10-Year Cardiovascular Risk: Calculated using Framingham Risk Score and ACC/AHA Pooled Cohort Equations¹⁸

Statistical Analysis

Statistical analyses were performed using SPSS version 28.0 (IBM Corp, Armonk, NY) and R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria). The analysis plan was pre-specified and followed intention-to-treat principles.

Descriptive statistics

1. Continuous variables: Mean ± standard deviation for normal distributions; median (interquartile range) for non-normal distributions
2. Categorical variables: Frequencies and percentages
3. Normality assessed using Shapiro-Wilk test and visual inspection of Q-Q plots

Comparative analysis

1. Chi-square test or Fisher's exact test for categorical variables
2. One-way ANOVA with Turkey’s post-hoc test for normally distributed continuous variables
3. Kruskal-Wallis test with Dunn's post-hoc analysis for non-parametric data
4. Trend analysis using Cochran-Armitage test for ordered categorical outcomes

Multivariable analysis

1. Logistic regression to identify independent predictors of hypertension
2. Variables with p<0.20 in univariate analysis included in multivariable model
3. Model fit assessed using Hosmer-Lemeshow goodness-of-fit test
4. Multicollinearity assessed using variance inflation factors

Sample size and power

1. Post-hoc power analysis confirmed >90% power to detect observed differences
2. Effect sizes calculated using Cohen's conventions

Statistical significance was set at p<0.05 for all analyses, with Bonferroni correction applied for multiple pairwise comparisons.

Among 591 newly diagnosed T2DM patients, silent hypertension prevalence increased significantly with age: 11.2% in 18-40 years, 23.0% in 41-50 years, and 50.0% in ≥50 year’s groups (p<0.001). Mean systolic blood pressure increased progressively from 126.5 ± 12.4 mmHg among younger adults to 133.1 ± 16.8 mmHg in the oldest group but remained below the clinical hypertension cut-off of 140 mmHg, indicating subclinical vascular changes and increased cardiovascular risk among older patients (Table 1–3).

In adults aged ≥50 years, 73.2% met metabolic syndrome criteria, with high prevalence of dyslipidaemia and obesity. Logistic regression revealed age, BMI, HbA1c, family history of hypertension, and waist circumference as independent predictors of hypertension. Cardiovascular risk assessment indicated that older hypertensive patients had a higher 10-year risk compared to younger hypertensive.

Our study highlights a marked age-dependent increase in silent hypertension prevalence among newly diagnosed T2DM patients, doubling from young to middle-aged adults and reaching 50% in those ≥50 years. This proportion surpasses earlier reports from general populations likely due to the clinic-based high-risk cohort.^19–21 The rising systolic blood pressure, despite remaining within a conventionally normal range, may reflect early arterial stiffening and increased vascular risk, consistent with pathophysiological concepts of synergistic vascular aging and diabetes impact.^23,24 The high prevalence of metabolic syndrome in older adults—well over 70%—combined with frequent dyslipidaemias and obesity, underscores clustering of cardiovascular risk factors in newly diagnosed diabetics and supports multifactorial intervention approaches.^22,29 Early hypertension detection coupled with risk factor modification could exploit the UKPDS legacy effect of sustained cardiovascular protection, emphasizing the need for vigilant screening from the time of diabetes diagnosis.¹²

Our multivariable analysis identifies practical predictors that could assist clinicians in risk stratification and prioritization for early hypertension screening and management. The study’s results advocate for age-stratified management algorithms and comprehensive cardiometabolic care to reduce future cardiovascular events effectively.^25–28,30

This study's cross-sectional design precludes causal inference and temporal analysis of hypertension development. Participants were recruited from diabetes specialty clinics in India; thus, findings may not generalize to other ethnic groups or community-based populations. Additionally, the absence of a healthy control group limits comparative prevalence assessment. Blood pressure was measured at a single time point, which could miss variability, although standardized protocols were used to mitigate this. Lastly, some lifestyle and socioeconomic factors were not systematically assessed, which may influence results.

An alarming half of older adults with newly diagnosed T2DM harbour undiagnosed hypertension, coupled with high cardiometabolic risk factor clustering. Early, age-appropriate screening and comprehensive risk management are critical to leveraging therapeutic windows for cardiovascular prevention in diabetes. Our findings advocate for guideline updates emphasizing age-stratified hypertension screening and integrated cardiometabolic care at diabetes diagnosis.

We thank all study participants for their valuable contribution to advancing our understanding of cardiovascular risk in diabetes. We acknowledge the dedication of our clinical research teams at both study sites, including research coordinators, laboratory personnel, and nursing staff who ensured rigorous data collection and participant care.

S.P., A.M conceived the study concept, designed the protocol, supervised data collection at Mumbai site, performed statistical analysis, and drafted the manuscript. S.S., A.M contributed to study design, supervised data collection at Patiala site, participated in data interpretation, and critically revised the manuscript. A.R. contributed to statistical analysis planning, performed data management and quality assurance, and assisted with manuscript preparation. All authors approved the final manuscript and take responsibility for data integrity and analysis accuracy.

This research was conducted as an investigator-initiated study utilizing institutional resources for algorithm development and clinical validation. No pharmaceutical industry funding was received. All authors declare no financial conflicts of interest related to this research.

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