Alpha-klotho, insulin resistance, and adiposity among older adults

Anam Ahmad; Michelle Shardell; Rita Rastogi Kalyani; Richard D Semba; Braxton D Mitchell; Toshiko Tanaka; Simeon Taylor; Aman Shrestha; Luigi Ferrucci

doi:10.1210/jendso/bvag140

. 2026 Jun 24;10(7):bvag140. doi: 10.1210/jendso/bvag140

Alpha-klotho, insulin resistance, and adiposity among older adults

Anam Ahmad ¹, Michelle Shardell ^2,^3,^✉, Rita Rastogi Kalyani ⁴, Richard D Semba ⁵, Braxton D Mitchell ^6,⁷, Toshiko Tanaka ⁸, Simeon Taylor ^9,¹⁰, Aman Shrestha ^11,¹², Luigi Ferrucci ¹³

PMCID: PMC13334091 PMID: 42440939

Abstract

Context

Alpha-klotho is a circulating protein linked to better metabolic and aging-related outcomes. However, alpha-klotho's relationship with insulin resistance, and the role of adiposity, remain unclear, especially among older adults.

Objective

To evaluate the association of circulating alpha-klotho with measures of insulin resistance and incident diabetes among older adults and examine whether these relationships differ by body mass index (BMI).

Main outcome measures

The primary outcome was Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Secondary outcomes were serum fasting glucose and insulin, and incident diabetes.

Design, setting, and participants

This secondary analysis examined associations of alpha-klotho with outcomes among 825 community-dwelling adults aged ≥55 years enrolled in the Invecchiare in Chianti Study, a prospective population-based cohort. Alpha-klotho and outcomes were assessed 3 years after enrollment. We performed BMI-stratified regression analysis, with adjustment for covariates, including measures of glucose and insulin taken at enrollment.

Results

Among 370 overweight participants (BMI 25.0-29.9 kg/m²), each logarithm unit of alpha-klotho (pg/mL) was associated with 0.51 higher HOMA-IR (95% confidence interval [CI], 0.07-0.94), after covariate adjustment. Alpha-klotho was significantly associated with serum glucose (Beta = 0.56, 95% CI: 0.04-1.09), but not serum insulin after covariate adjustment. No significant associations were found among participants with BMI < 25.0 (n = 279) or ≥30 kg/m² (n = 124) (P for interaction = 0.18). Alpha-klotho was not significantly associated with incident diabetes, overall or in any BMI subgroup.

Conclusion

Alpha-klotho was positively associated with insulin resistance, but not incident diabetes, among overweight adults. Findings suggest that alpha-klotho may have a context-dependent relationship with insulin resistance, but not incident diabetes.

Keywords: alpha-klotho, insulin resistance, HOMA-IR

The Klotho gene (KL), located on chromosome 13q12 in humans, encodes for alpha-klotho, a single-pass transmembrane protein that functions as a co-receptor for fibroblast growth factor 23 (FGF-23) [1]. Alpha-klotho exists in 3 biologically distinct forms: (1) its aforementioned membrane-bound form predominantly expressed in the distal convoluted tubule of the kidney and the choroid plexus of the brain [2], (2) as a soluble protein cleaved from the membrane-bound form by membrane-bound proteases, and (3) in a secreted form produced from alternative mRNA splicing [3]. The soluble and secreted forms circulate in blood, cerebrospinal fluid, and urine, suggesting that klotho may have systemic roles beyond its membrane-bound role as a co-receptor—although these roles and their underlying mechanisms are still being identified [4].

Among these emerging systemic roles, one of the most studied pertains to aging biology. Genetic variants in the Klotho gene are linked to differences in longevity and brain health. Mice lacking Kl exhibit premature aging, including arteriosclerosis and shortened lifespans [1], while mice with overexpression of Kl display extended lifespans [5]. In humans, lower circulating alpha-klotho concentrations are similarly associated with an increased mortality risk and worse aging-related outcomes, including increased odds of frailty, greater cognitive decline, and Alzheimer's disease [4, 6-12].

Beyond its associations with aging biology, alpha-klotho has also been linked to glucose homeostasis and insulin signaling. This link is important because impaired insulin signaling is a strong correlate of accelerated aging and is associated with adverse health outcomes, including increased diabetes risk and reduced lifespan [13]. However, findings in this area have been inconsistent. Some studies report a paradoxical positive association between alpha-klotho and insulin resistance, while others observe inverse associations with insulin resistance or metabolic syndrome [14-16]. A recent literature review highlighted the difficulty of integrating existing findings into a coherent narrative and attempted to reconcile conflicting findings by hypothesizing that klotho levels required for metabolic homeostasis may vary according to the level of cellular stress [17]. This builds on an early hypothesis in the field which suggested that alpha-klotho may induce insulin resistance as a protective mechanism to limit oxidative stress in hyperglycemic settings by preventing uptake of excessive glucose [18]. However, another study raised the possibility that insulin action increases circulating alpha-klotho, though this reverse causality explanation has little support [19]. Overall, existing evidence suggests that the relationship between alpha-klotho and insulin resistance is complex and may vary across physiological contexts.

Adiposity may represent a key factor underlying this context dependence. Alpha-klotho has been shown to induce insulin resistance in 3T3-L1 adipocytes in vitro, suggesting that adipose tissue may play a key role in mediating alpha-klotho's metabolic effects [20]. Furthermore, higher body mass index (BMI), a surrogate for adiposity and potential contributor of intracellular oxidative stress and metabolic dysregulation, has been consistently associated with lower levels of alpha-klotho [21]. The growing body of literature on alpha-klotho and insulin resistance has yet to conclusively resolve this question. Most studies have relied primarily on cross-sectional data, and few have examined whether adiposity modifies alpha-klotho–insulin resistance relationship, particularly among older adults. Moreover, although alpha-klotho is implicated in pathways relevant to diabetes pathophysiology, its relationship with diabetes risk has not been fully characterized. One cross-sectional study from the National Health and Nutrition Examination Study (NHANES) reported that alpha-klotho was more strongly positively correlated with insulin resistance among people without prevalent diabetes than people with prevalent diabetes, while another study of NHANES participants showed a U-shaped relationship between alpha-klotho and prevalent diabetes [14, 22]. Important gaps include the lack of research that addresses the potential reverse causality between alpha-klotho and insulin resistance, and the lack of data evaluating whether circulating alpha-klotho concentrations are associated with incident diabetes.

To address these gaps, we examined the relationship between plasma alpha-klotho and measures of insulin resistance among older adults and tested whether this relationship differs by BMI, even after adjustment for earlier levels of insulin resistance. Because alpha-klotho is closely linked to biological aging, and insulin resistance becomes increasingly prevalent with age, older adults represent a key population in which to investigate alpha-klotho's metabolic effects. Furthermore, while adiposity is a major driver of insulin resistance, it does not fully explain the substantial metabolic variation observed in older adults. This gap highlights the importance of identifying additional biological factors that may contribute to age-related insulin resistance and understanding how these factors may interact with adiposity to shape metabolic risk. We hypothesized that plasma alpha-klotho concentrations would be positively associated with insulin resistance and that this association would strengthen with greater BMI. We also tested the hypothesis that plasma alpha-klotho concentrations would be associated with incident (newly diagnosed) diabetes.

Materials and methods

Setting and participants

The Invecchiare in Chianti (English translation: “Aging in Chianti”) study (“InCHIANTI”) is a longitudinal prospective cohort study comprising adults who were recruited from 2 sites in the Chianti region of Italy: Greve in Chianti and Bagno a Ripoli. A random sample of 650 individuals aged 65 years or older was identified from the population registry at each of the 2 sites via a 2-stage sampling procedure and approached to join the study. Additional samples of 50 men and 50 women were identified for each age stratum (20-29, 30-39, 40-49, 50-59, and 60-64 years) via the same methodology in each site, resulting in 1453 participants enrolled overall [23]. All participants received detailed information about the study and provided written informed consent. The study was approved by both the Italian National Institute of Research and Care on Aging Ethical Committee and the Johns Hopkins Institutional Review Board.

The present study included data from the baseline visit, which was conducted between 1998 and 2000, and the first follow-up visit, which occurred 3 years after baseline (between 2001 and 2003). Among the 1453 participants of InCHIANTI who enrolled in the study at the baseline visit, 1167 returned for the first follow-up visit. All participants who were aged 55 years or older at the first follow-up visit and did not have missing data for the exposure and outcome variables, described below, were included in the analytic cohort. After excluding participants with missing covariates or outcomes, the final analytic cohort comprised 825 participants, with ages ranging from 55 to 98 years old at the follow-up visit (Fig. 1).

For image description, please refer to the figure legend and surrounding text. — Flow diagram describing the final InCHIANTI cohort sample that was included for statistical analyses. 1453 participants enrolled in the study at the baseline visit. Of these individuals, 286 were lost to follow-up, of whom 144 died before their first follow-up visit. Of the cohort that returned for the 3-year follow-up visit, 985 individuals were aged 55 years or older. Only 825 individuals had non-missing values for the exposure (alpha-klotho), primary outcome (HOMA-IR, Homeostatic Model Assessment for Insulin Resistance), and subgroup variable of interest (BMI) at this follow-up visit.

Study measures

Biomarkers

All biomarkers were analyzed using samples collected during the follow-up visit (3-years after baseline) unless specified otherwise. Morning blood samples were taken after a 12-hour fasting period. Serum and plasma aliquots were rapidly prepared and stored at −80 °C.

Exposure

Soluble plasma alpha-klotho was quantified in EDTA plasma collected at the follow-up visit utilizing a solid-phase sandwich enzyme-linked immunosorbent assay (Immuno-Biological Laboratories, Takasaki, Japan). The assay had a detection limit of 6.15 pg/mL and intra-assay and interassay coefficients of variation of 4.1% and 8.9%, respectively.

Outcomes

The primary outcome measure was homeostasis model for insulin resistance (HOMA-IR), a measure of insulin resistance from patient fasting serum glucose and insulin measures using the following equation:

HOMA - IR = \frac{glucose (mmol / L) \times insulin (mIU / L)}{22.5}

Fasting serum insulin (pmol/L; where 1 mIU/L = 6.00 pmol/L) and blood glucose (mmol/L) were also assessed individually as secondary outcomes [24].

Serum glucose was measured (Laboratory of Clinical Chemistry and Microbiological Assays, SS. Annunziata Hospital, Azienda Sanitaria 10, Florence, Italy) through an automated system with an enzymatic colorimetric assay using the glucose-oxidase-peroxidase-chromogen reaction and a Roche analyzer (Roche Diagnostics, GmbH, Mannheim, Germany). Baseline measures were analyzed with a Hitachi 917 model, and measures collected at the first follow-up were analyzed with a Modular P800 Hitachi. The lower detection limit was (SI units in parentheses) 2 mg/dL (0.111 mmol/L); the intra-assay and interassay coefficients of variation were 0.9% and 1.8%, respectively.

Serum insulin was quantified by the laboratory of the Department of Geriatric Medicine and Metabolic Diseases VI, Unit of Internal Medicine, Second University of Naples, Naples, Italy. Measures collected at the baseline visit were quantified in EDTA plasma using a commercial double-antibody, solid-phase radioimmunoassay kit (Sorin Biomedica, Milano, Italy), with a sensitivity of 1.0 mIU/L (6.00 pmol/L) and an intra-assay coefficient of variation of 3.1%. Measures collected at the first follow-up visit were quantified in serum using a commercial double-antibody, solid-phase immunoradiometric (IRMA) assay (INS-IRMA, Sorin Biomedica, Milano, Italy), with a sensitivity of 1.0 mIU/L (6.00 pmol/L). Intra-assay coefficients of variation were 2.2% and 1.6% for 2 different concentrations; inter-assay coefficients of variation were 6.5% and 6.1% for 2 different concentrations.

Secondary outcomes included serum insulin and glucose at the first follow-up, as described above, and new diagnoses of diabetes at follow-up. Individuals were classified as having diabetes if they self-reported taking diabetes medications (insulins, insulin analogs, or oral hypoglycemics), had a measured fasting serum glucose ≥126 mg/dL (7.0 mmol/L), or if they self-reported a history of diabetes and indicated current treatment/management through diet.

Other biomarkers

Serum creatinine concentrations were measured using a kinetic-colorimetric assay based on a rate-blanked and compensated modified Jaffé method for a Roche/Hitachi analyzer (Roche Diagnostics, GmbH, Mannheim, Germany) with intra-assay and interassay coefficients of variation of 0.7% and 2.3%, respectively. Creatinine values were standardized to estimate glomerular filtration rate (measured in mL/min/1.73 m²) using the Chronic Kidney Disease Epidemiology Collaboration equation [25].

Serum 25-hydroxyvitamin D [25(OH)D] was measured using an enzyme immunoassay (OCTEIA 25-Hydroxy Vitamin D kit; Immunodiagnostic Systems, Inc., Fountain Hills, AZ) with intra-assay and interassay coefficients of variation ranging 5.3% to 6.7% and 4.6% to 8.7%, respectively.

Serum intact PTH concentrations were measured with 2-site chemiluminescent enzyme-labeled immunometric assay (Intact PTH; Diagnostic Products Corporation, Los Angeles, CA) and an IMMULITE 2000 Analyzer (Siemens Medical Solutions Diagnostics, Tarrytown, NY) with intra-assay and interassay coefficients of variation ranging 4.2% to 5.7% and 6.3% to 8.8%, respectively.

Other covariates

All covariates were collected at follow-up visit (3 years after baseline), unless otherwise specified. Demographic variables consisted of age (years), sex, and education (years of schooling). Lifestyle factors included smoking history (estimated pack-years) as well as alcohol consumption (drinks/week) and calcium intake (mg/day), which were assessed using the European Prospective Investigation into Cancer and Nutrition questionnaire. Comorbidities (hypertension, congestive heart failure, peripheral artery disease, stroke, cancer, renal disease, and osteoporosis) were all assessed using adjudicated measures combining self-report, medical records, and clinical examination. For congestive heart failure, concurrent use of a diuretic (unspecified or an aldosterone antagonist) together with an angiotensin II antagonist, ACE inhibitor, or digitalis glycoside was included as supplementary evidence in addition to reported history, documented diagnosis, or physical examination findings. Cognitive function was assessed using the Mini-Mental State examination (range: 0-30) [26]. Baseline serum glucose and insulin were collected in fasting blood draws and measured as described above for inclusion as covariates to rule out reverse causality. Baseline diabetes, operationalized as described above, was also assessed to identify participants at risk for incident diabetes.

BMI was calculated (kg/m²) based on measured height (m) and weight (kg). Body weight was measured using a calibrated beam balance scale, with participants wearing light clothing and no shoes, preferably in a fasting state. Height was measured with a stadiometer in the Frankfurt horizontal plane, standing upright, when possible, with the participant's back and heel touching the measuring bar. Alternative standardized procedures were used for individuals with kyphosis or paraplegia.

BMI was operationalized as a categorical variable for subgroup analysis (described below) and classified via the following World Health Organization standard index categories: low-normal (BMI < 25.0 kg/m²), overweight (25.0 kg/m² to 29.9 kg/m²), and obese (≥30 kg/m²). Low and normal BMI categories were combined owing to the small number considered low (N = 11; BMI < 18.5 kg/m²). We also performed a sensitivity analysis excluding participants with low BMI. Although BMI has some known limitations, our decision to use it was based on the established thresholds, its consistent use in alpha-klotho research, and its empirical characteristics in InCHIANTI. Namely we compared BMI to other measures of adiposity available in InCHIANTI (waist circumference, trifold skinfold thickness, and Body Roundness Index [BRI], which is calculated from height and waist circumference) and found that BMI consistently correlated strongly with the other measures and had the most complete data (Supplemental Tables S1 and S2) [27].

Statistical analysis

Participant characteristics were compared by plasma alpha-klotho concentration split at the median value (673 pg/mL). Comparisons were made using 2-sample t-tests or Wilcoxon ranked sum-tests for continuous variables and Fisher's exact tests for categorical variables. Scatter plots were generated to visualize the association between alpha-klotho and HOMA-IR across a continuous BMI gradient, both overall and stratified by BMI classification. LOESS smoothing curves were overlaid to aid visualization of the associations. Plots were generated using R software, version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria) using the ggplot2 package.

Linear regression models were used to quantify the relationship of plasma alpha-klotho concentration with HOMA-IR assessment (primary outcome) and with serum insulin and glucose (secondary outcomes), with and without adjustment for covariates described above, including baseline serum insulin and glucose. Alpha-klotho and 25(OH)D were log-transformed in statistical analyses. BMI and BMI² were included as continuous variables to address potential non-linearity. Subgroup analyses by BMI category (low-normal, overweight, obese) were prespecified based on scientific rationale and prior literature [28, 29]. The model was refit with alpha-klotho-by-BMI category (low-normal, overweight, and obese) interaction terms, and a final set of models was fit stratified by BMI category with additional adjustment for continuous BMI to reduce residual confounding. Additionally, logistic regression was used to examine the association between alpha-klotho and newly diagnosed cases of diabetes at the 3-year visit among participants who did not have diabetes at the baseline visit.

We compared key characteristics between participants excluded for loss to follow-up or missing key variables and those who were included in the analytic cohort to assess potential selection bias. For all regression models, inverse probability weighting was used to address missing data. Briefly, indicators of complete covariate data, survival to the follow-up visit, and nonmissing outcomes were logistically regressed on covariates with no missing values in separate models. Reciprocals of the estimated probabilities for complete covariates, survival, and observed outcomes were multiplied together and used as weights in regression models [30]. For all statistical analysis, P-value < .05 or 95% confidence intervals (CI) excluding the null were considered statistically significant. For tests of interaction, P-value < .20 was considered indicative of statistically meaningful interaction, per published recommendations [31, 32]. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).

Additional sensitivity analyses were performed to assess the robustness of the observed findings. These included models adjusting for medications known to affect insulin resistance, including diabetes medications, serum lipid reducing agents, diuretics, beta-blocking agents, immunosuppressive agents, sex hormones, corticosteroids, and atypical antipsychotics [33-39]. Sensitivity analyses also accounted for baseline diabetes, first by adjusting for baseline diabetes status, and then by excluding n = 92 participants with baseline diabetes.

Results

Table 1 shows that participants with plasma alpha-klotho concentration greater than the median 673 pg/mL were on average younger (74.2 vs 75.8 years), consumed less alcohol (6.4 vs 7.8 drinks/week), had a lower prevalence of congestive heart failure (4.1 vs 7.7%), and had higher serum insulin concentrations (62.9 vs 58.9 pmol/L) than participants with alpha-klotho concentrations below the median (all P < .05). Additionally, there were progressively higher mean HOMA-IR, serum glucose, and serum insulin across BMI categories (Table S3) [27]. Participants who were excluded from the analytic dataset because of loss to follow-up or missing data in key variables differed from those retained in the cohort in several characteristics (Table S4) [27]. In general, excluded participants were significantly older at baseline (78.3 vs 71.9 years) and exhibited indicators of poorer health.

Table 1.

Characteristics of 825 InChianti study participants by klotho concentration (median = 673 pg/mL)

Characteristic	N	Alpha-Klotho ≤ 673 pg/mL (N = 413) mean (SD), number (%), or median (IQR)	N	Alpha-Klotho > 673 pg/mL (N = 412) mean (SD), number (%), or median (IQR)	P-value
Age (years), mean (SD)	413	75.8 (7.9)	412	74.2 (7.4)	.002
Female sex, number (%)	413	213 (51.6%)	412	241 (58.5%)	.05
Education (years), mean (SD)	413	5.9 (3.9)	412	5.7 (3.2)	.38
Smoking (pack-years), mean (SD)	412	12.9 (19.6)	412	11.2 (19.3)	.21
Alcohol consumption (drinks/wk), mean (SD)	388	7.8 (9.0)	390	6.4 (8.4)	.03
Body Mass Index (kg/m²), mean (SD)	413	26.4 (4.0)	412	26.5 (3.9)	.66
Mini-Mental State Examination score, mean (SD)	411	25.0 (4.6)	411	25.6 (4.3)	.07
Hypertension, number (%)	413	132 (32.0%)	412	148 (35.9%)	.24
Congestive heart failure, number (%)	413	32 (7.7%)	412	17 (4.1%)	.04
Peripheral arterial disease, number (%)	413	40 (9.7%)	412	36 (8.7%)	.72
Baseline diabetes, number (%)	413	49 (11.9%)	412	43 (10.4%)	.58
Diabetes, number (%)	413	63 (15.3%)	412	56 (13.6%)	.55
Renal disease, number (%)	413	265 (64.2%)	412	279 (67.7%)	.30
Osteoporosis, number (%)	413	87 (21.1%)	412	99 (24.0%)	.32
Estimated glomerular filtration rate (mL/min/1.73 m²), median (IQR)	413	67.0 (55.5, 80.2)	412	67.5 (56.5, 79.5)	.51
Calcium intake (mg/day), median (IQR)	388	824.7 (673.7,1033.3)	390	793.9 (629.8, 1022.0)	.19
Parathyroid Hormone (pmol/L), median (IQR)	412	4.2 (3.1, 6.0)	412	4.4 (3.1, 6.4)	.27
25-hydroxyvitamin D (nmol/L), median (IQR)	412	76.6 (49.5, 116.8)	412	74.6 (53.7, 128.9)	.50
Klotho (pg/mL), median (IQR)	413	527.1 (447.4, 601.9)	412	813.2 (742.1, 929.9)
HOMA-IR, median (IQR)	413	2.3 (1.6, 3.5)	412	2.5 (1.8, 3.4)	.07
Glucose (mmol/L), median (IQR)	413	5.1 (4.7, 5.6)	412	5.0 (4.6, 5.6)	.50
Insulin (pmol/L), median (IQR)	413	58.9 (43.1, 84.9)	412	62.9 (49.0, 88.0)	.02
Baseline glucose (mmol/L), median (IQR)	413	4.9 (4.6, 5.5)	412	4.9 (4.5, 5.4)	.11
Baseline insulin (pmol/L), median (IQR)	413	57.4 (37.4, 84.0)	412	60.0 (42.4,87.0)	.09

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IQR = interquartile range. N denotes the number of individuals with nonmissing data. Continuous variables are compared using 2-sample t tests or Wilcoxon rank-sum tests; categorical variables are compared using Fisher's exact tests. Mini-mental Status Examination scores range from 0 to 30.

All measures were collected at the follow-up visit except for baseline glucose, baseline insulin, and baseline diabetes, which were measured at the initial (baseline) visit. glucose (mmol/L) = (mg/dL) ÷ 18; insulin (pmol/L) = (mIU/L) × 6.00; vitamin D (nmol/L) = (ng/mL) × 2.496; and parathyroid hormone (PTH) (pmol/L) = (pg/mL) × 0.1061.

Scatterplots of alpha-Klotho and HOMA-IR, displayed across a continuous BMI color gradient (overall and stratified by BMI category) are shown in Fig. 2. The overall pattern suggested a modest positive linear trend, with similar positive associations in the low-normal and overweight BMI subgroups (Fig. 2A–2C). In contrast, the obese subgroup displayed greater variability and a slightly curved but otherwise negative trend (Fig. 2D). Despite this variability, the LOESS curves demonstrated minimal deviation from linearity across BMI categories, supporting the use of linear regression models to analyze these associations.

In unadjusted models, plasma alpha-klotho was significantly positively associated with HOMA-IR (Beta = 0.44; 95% CI, 0.07, 0.80; P = .02) and serum insulin (Beta = 7.32, 95% CI, 0.50, 14.15, P = .04). After adjustment for covariates, including baseline serum glucose and baseline insulin, these findings were attenuated and no longer statistically significant for both HOMA-IR (Beta = 0.13, 95% CI, −0.17, 0.43, P = .39) and insulin (Beta = −0.94, 95% CI, −5.41, 3.53, P = .68). Alpha-klotho was not significantly associated with fasting serum glucose in either the unadjusted or adjusted models (Table 2).

Table 2.

Association between the exposure (alpha-klotho), and primary (HOMA-IR) and secondary outcomes (serum glucose and insulin) with and without adjustment for covariates

Model	Outcome	Beta estimate^a	95% confidence interval	P-value
Unadjusted	HOMA-IR	0.44	(0.07, 0.80)	.02
	Glucose (mmol/L)	0.22	(−0.15, 0.60)	.24
	Insulin (pmol/L)	7.32	(0.50, 14.15)	.04
Adjusted^b	HOMA-IR	0.13	(−0.17, 0.43)	.39
	Glucose (mmol/L)	0.27	(−0.07, 0.61)	.12
	Insulin (pmol/L)	−0.94	(−5.41, 3.53)	.68

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^aBeta estimates represent the mean difference of the outcome variable per ln(alpha-klotho) in pg/mL.

^bModels were adjusted for age, sex, BMI, education, alcohol intake, mini-mental status examination score, parathyroid hormone concentration, 25(OH)D, congestive heart failure, alcohol consumption, smoking, hypertension, peripheral artery disease, osteoporosis, estimated glomerular filtration rate, calcium intake, baseline serum insulin and baseline plasma glucose. glucose (mmol/L) = (mg/dL) ÷ 18; insulin (pmol/L) = (mIU/L) × 6.00.

After stratifying by BMI category, descriptive analysis (Table S5) [27] demonstrated that among overweight participants, mean HOMA-IR, serum glucose, and serum insulin were higher among those with plasma alpha-klotho >673 pg/mL vs alpha-klotho ≤673 pg/mL [27]. Moreover, plasma alpha-klotho levels were significantly and positively associated with HOMA-IR among overweight participants in both unadjusted (Beta = 0.77, 95% CI, 0.25, 1.29, P = .004) and covariate-adjusted (Beta = 0.51, 95% CI, 0.07, 0.94, P = .02) models (Table 3). Although not statistically significant (P > .05), the direction of association was positive among low-normal weight participants and negative among participants with obesity, and the interaction between alpha-klotho and BMI category was statistically significant after adjusting for covariates in the HOMA-IR model based on the threshold for interaction P < .20 (P = .18). When evaluating the secondary outcomes serum glucose and serum insulin as individual outcomes, the unadjusted association between alpha-klotho and serum glucose in the overweight stratum was not statistically significant but became significant after adjusting for covariates (Beta = 0.56, 95% CI, 0.04, 1.09, P = .04). The unadjusted association between alpha-klotho and serum insulin in the overweight stratum was statistically significant (Beta = 12.34, 95% CI, 3.21, 21.47, P = .01) but was attenuated and no longer statistically significant after adjustment for covariates.

Table 3.

Unadjusted and adjusted associations between alpha-klotho and outcomes HOMA-IR, serum glucose, and serum insulin, stratified by BMI category

Outcome	Model	Classification	N	Beta estimate^a	95% confidence interval	P-value	P for interaction
HOMA-IR	Unadjusted	Low-Normal (BMI <25 kg/m²)	279	0.36	(−0.04, 0.75)	.08	.30
		Overweight (BMI 25–29.9 kg/m²)	370	0.77	(0.25, 1.29)	.004
		Obese (BMI ≥ 30 kg/m²)	124	−0.10	(−1.38, 1.17)	.87
	Adjusted^b	Low-Normal (BMI <25 kg/m²)	279	0.11	(−0.20, 0.43)	.49	.18
		Overweight (BMI 25-29.9 kg/m²)	370	0.51	(0.07, 0.94)	.02
		Obese (BMI ≥ 30 kg/m²)	124	−0.22	(−1.17, 0.73)	.65
Glucose (mmol/L)	Unadjusted	Low-Normal (BMI <25 kg/m²)	279	0.26	(−0.17, 0.68)	.23	.20
		Overweight (BMI 25-29.9 kg/m²)	370	0.46	(−0.14, 1.07)	.13
		Obese (BMI ≥ 30 kg/m²)	124	−0.47	(−1.63, 0.70)	.43
	Adjusted^b	Low-Normal (BMI <25 kg/m²)	279	0.16	(−0.22, 0.54)	.40	.44
		Overweight (BMI 25-29.9 kg/m²)	370	0.56	(0.04, 1.09)	.04
		Obese (BMI ≥ 30 kg/m²)	124	0.25	(−0.60, 1.09)	.57
Insulin (pmol/L)	Unadjusted	Low-Normal (BMI <25 kg/m²)	279	5.61	(−2.51, 13.74)	.18	.67
		Overweight (BMI 25-29.9 kg/m²)	370	12.34	(3.21, 21.47)	.01
		Obese (BMI ≥ 30 kg/m²)	124	1.14	(−23.58,25.85)	.93
	Adjusted^b	Low-Normal (BMI <25 kg/m²)	279	−0.33	(−5.82, 5.16)	.91	.21
		Overweight (BMI 25-29.9 kg/m²)	370	5.36	(−0.65, 11.37)	.08
		Obese (BMI ≥ 30 kg/m²)	124	−12.46	(−28.58, 3.65)	.13

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Models only included participant with complete data for all covariates. Stratified models excluded 20 individuals in the low-normal BMI stratum, 22 individuals in the overweight stratum, and 10 individuals in the obese stratum due to incomplete data.

^aBeta estimates represent the mean difference of the outcome variable per ln(alpha-klotho) in pg/mL.

^bModels were adjusted for age, sex, BMI, education, mini-mental status examination score, parathyroid hormone concentration, 25(OH)D, congestive heart failure, alcohol consumption, smoking, hypertension, peripheral artery disease, osteoporosis, estimated glomerular filtration rate, calcium intake, baseline serum insulin and baseline plasma glucose. glucose (mmol/L) = (mg/dL) ÷ 18; insulin (pmol/L) = (mIU/L) × 6.00.

These findings were consistent after additional adjustment for medications known to influence insulin resistance (Tables S6 and S7) [27] and after adjustment for baseline diabetes (Tables S8 and S9) [27]. However, the associations were largely attenuated when the analytic sample was reduced by excluding 92 participants with baseline diabetes (Tables S10 and S11) [27]. Findings in the low-normal BMI group were also consistent after excluding the small sample of participants with BMI < 18.5 kg/m² (Table S12) [27].

Among 690 participants with complete data who did not have diabetes at baseline, there were 25 newly diagnosed diabetes cases (3.6%) (Figure) [27]. The odds of being diagnosed with diabetes between the baseline visit and first follow-up visit was low and not significantly associated with alpha-klotho in unadjusted (P = .47) and adjusted (P = .35) models (Table 4).

Table 4.

Assessing the association between alpha-klotho and odds of incident diabetes over a 3-year follow-up period

Model	Odds ratio^a	95% confidence interval	P-value
Unadjusted	1.34	(0.61, 2.98)	.47
Adjusted^b	1.65	(0.58, 4.71)	.35

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Six hundred and ninety subjects without diabetes at baseline were included in this analysis, of which 25 developed diabetes during the 3-year follow-up.

^aOdds ratio represents the odds of developing diabetes for each 1 unit higher of ln(klotho) in pg/mL.

^bModel was adjusted for age, sex, BMI, education, mini-mental status examination score, parathyroid hormone concentration, 25(OH)D, congestive heart failure, alcohol consumption, smoking, hypertension, peripheral artery disease, osteoporosis, estimated glomerular filtration rate, calcium intake, baseline serum insulin, and baseline plasma glucose.

Discussion

Overall, our results do not provide consistently strong support for a relationship between alpha-klotho and insulin resistance. In unadjusted models, alpha-klotho was associated with higher HOMA-IR and insulin concentrations in the whole sample, but these findings were attenuated and not statistically significant after adjustment for covariates. We observed a positive association between alpha-klotho and HOMA-IR as a measure of insulin resistance among overweight participants that remained statistically significant even after adjustment for covariates, including baseline diabetes and medications. Alpha-klotho was also positively associated with glucose concentrations among overweight participants after adjustment for these covariates.

Although not all statistically significant, there was a pattern of mostly positive associations between alpha-klotho and measures of insulin resistance, except among participants with obesity. The lack of association between alpha-klotho and HOMA-IR among participants with obesity may be due to the effect of alpha-klotho being blunted within a population that tends to have higher rates of metabolic dysregulation [40]. Furthermore, obesity-related biological mechanisms could plausibly attenuate alpha-klotho signaling. Obesity is characterized by elevated free fatty acids, oxidative stress, and chronic inflammation [41]. While some studies suggest that alpha-klotho downregulates inflammatory markers, these markers are notably elevated in obesity and may, in turn, suppress alpha-klotho expression through a negative feedback loop. Consistent with this possibility, Du et al [42] reported inflammatory markers mediated the inverse association between alpha-klotho and BRI in a large NHANES cohort of adults aged 40 to 79 years.

Additionally, we did not observe a significant association with incident diabetes in our study. This finding aligns with previous reports and suggests that alpha-klotho may not be a strong independent risk factor for clinical outcomes [17, 22, 43, 44]. However it is important to acknowledge that mechanistic studies have implicated alpha-klotho in pancreatic beta-cell function independently of its role in insulin signaling [45, 46]. Given that type 2 diabetes results primarily from progressive beta-cell failure, our finding cannot completely rule out alpha-klotho as playing a meaningful role in type 2 diabetes [47]. Indeed, the absence of an observed association in our study may reflect limited statistical power due to the low incidence of diabetes in this older adult population.

Understanding insulin signaling is crucial for identifying endocrine functions that can influence the pace of biological aging. Present evidence from in vitro and in vivo studies suggest that alpha-klotho may independently inhibit the insulin/IGF signaling pathway, although the exact mechanism is still not understood and may be context dependent. Kurosu et al proposed that alpha-klotho prevents phosphorylation of insulin and IGF-1 receptors, in turn reducing activation of insulin receptor substrate-1 and further downstream signaling [5]. However, Lorenzi et al [48] showed that soluble alpha-klotho did not affect insulin signaling in insulin-sensitive cells directly, although the authors were unable to rule out the possibility of indirect effects or that they did not use a sufficiently high alpha-klotho concentration in their experiment. Hassanejad et al [20] observed alpha-klotho suppressing insulin signaling in adipocytes in particular, and suggested that this effect may occur via interference of Glucose Transporter-4 (GLUT4) translocation or by preventing the phosphorylation of Akt, PFKfβ3, and GSK3β (3 main intracellular mediators of insulin signaling). While these findings suggest a mechanism of action in adipose tissue, it is important to note that GLUT4 is not expressed in the liver, and hepatic insulin resistance (which is more relevant to fasting glucose and HOMA-IR) may involve distinct mechanisms different from adipocyte models.

Observational studies in humans generally indicate inconsistent findings on alpha-klotho associations with insulin resistance and related conditions such as the metabolic syndrome but there is wide variability of results across studies [9, 21, 43, 49-52]. A 2023 study by Lefta et al [50] reported a significant negative association between alpha-klotho and HOMA-IR, the same metric used to assess insulin resistance in our study. However, these findings were obtained from a small cohort of 90 middle-aged and older adult patients with diabetic nephropathy that may not be generalizable to community-dwelling older adults. Two cross-sectional studies published in 2022 using data from NHANES, 1 studying adults 18 years and older, and the other studying adults aged 40 to 79 years old, also reported significant negative associations between alpha-klotho and metabolic syndrome [21, 43]. However, metabolic syndrome, while closely associated with insulin resistance, is not a direct measure or proxy of insulin resistance. In contrast, three 2024 studies using NHANES data, also among adults aged 18 years and older and among adults aged 40-79 years, found positive associations between alpha-klotho and insulin resistance measured using HOMA-IR [14-16]. Lastly, Socha-Banasiak et al [49] reported a positive association between alpha-klotho and HOMA-IR; however, this study was in a small sample of 174 hospitalized children, which cannot be assumed to be generalizable to community-dwelling older adults.

The current literature suggests that alpha-klotho has a complex relationship with adiposity, and it is plausible that this relationship intersects with alpha-klotho's role in insulin signaling. Alpha-klotho has been shown to play a role in inducing adipocyte differentiation during development [53]. However, it has also been shown to inhibit insulin signaling in differentiated adipocytes [20]. Moreover, the relationship between alpha-klotho and adiposity has been inconsistent in cohort studies. Interestingly, Huang et al [52] reported a positive association between alpha-klotho and sagittal abdominal diameter, particularly in individuals with obesity, across ages 40 to 79. Amaro-Gahete et al [54] observed a positive association between alpha-klotho and BMI that disappeared after controlling for lean mass across participants aged 40 to 65 years; fat mass was not significantly associated with alpha-klotho although this may have been due to low power (n = 74) . Other studies have observed reduced circulating concentrations of alpha-klotho in adults with obesity [21, 43, 55, 56]. These findings suggest that variability in adiposity and body composition may partially explain inconsistencies in the literature regarding the relationship between alpha-klotho and insulin resistance.

These issues may be especially relevant in our cohort of older adults. Prior studies have largely examined younger populations (on average 20 years younger than ours), whereas aging introduces sarcopenia (muscle wasting), a phenomenon that leads to concurrently decreased muscle mass and increased fat mass [57, 58]. Sarcopenia, both independently and in combination with obesity, is associated with insulin resistance [57]. In this context, BMI may underestimate adiposity; however, no widely accepted alternatives exist for this age group. Although BMI and sagittal abdominal diameter are known to be highly correlated, interpretation should still be cautious. Importantly, our study is among the few focused on older adults, providing insight not captured in previous studies [59].

This study had multiple strengths and fills key gaps in the literature. First, the InCHIANTI Study is a large well characterized cohort of primarily older Italian adults aged up to 98 years with rich data collection of relevant measures. As a result, it is the first study to control for baseline biomarker concentrations when assessing the association between alpha-klotho and diabetes incidence in this population. Such an evaluation is important for assessing a potential role for alpha-klotho in precision medicine. Second, this study is novel by being the first to explicitly stratify by BMI within a longitudinal cohort. Although the associations examined are cross-sectional, the longitudinal assessment of glucose and insulin allowed us to adjust for baseline concentrations measured 3-years prior, helping to mitigate reverse causality.

Despite these strengths, some limitations must be noted. First, like previous studies, alpha-klotho was only measured at 1 visit, thus changes in alpha-klotho could not be evaluated. Second, given that alpha-klotho and outcome measures of insulin resistance were measured at the same visit, possibility of reverse causality or negative feedback loops cannot be excluded completely. Indeed, research suggests that insulin may promote cleavage of transmembrane alpha-klotho into the soluble form, which would increase circulating alpha-klotho concentrations [19]. To mitigate the influence of this reverse causal mechanism on findings, we were able to adjust for baseline concentrations of both insulin and glucose. Furthermore, as with any observational study, we cannot rule out the potential for unmeasured confounding. Given the extensive data available in InCHIANTI, in addition to baseline insulin and glucose concentrations, we were able to adjust for multiple relevant covariates related to alpha-klotho regulation, insulin resistance, and aging to mitigate this issue. However, some identified regulators of alpha-klotho were not measured in our cohort and therefore remain a limitation; in particular, FGF-23 and phosphorous, compounds that are interrelated with alpha-klotho and may influence its regulation due to its role in the vitamin D synthesis pathway. Lastly, it is important to acknowledge that our interaction analyses and incident diabetes analysis may have been underpowered for modest associations due to the small sample size in the obese BMI category and low number of newly diagnosed diabetes cases respectively.

In summary, we found some evidence that alpha-klotho was independently and positively associated with insulin resistance as measured by HOMA-IR among overweight older adults, and that BMI classification significantly modified this relationship in the overall sample. These findings suggest that adiposity may influence the association between alpha-klotho and insulin resistance, but they do not indicate a broader or uniform metabolic role for alpha-klotho across BMI groups. Because significant associations were limited to the overweight category, and that other analyses did not yield consistent patterns, these results should be interpreted cautiously. While the observed pattern is compatible with the current accepted hypothesis that alpha-klotho may be a compensatory response to increasing inflammation and oxidative stress in overweight individuals, this interpretation remains speculative [17]. The findings highlight the need for replication in larger and more diverse cohorts, as well as studies with repeated alpha-klotho measurements to better clarify temporal relationships. Future longitudinal research can assess the relative timing of changes in alpha-klotho, insulin resistance, and adiposity and evaluate the merits of alpha-klotho as a target of intervention to regulate insulin signaling. As interest in alpha-klotho continues to grow in the context of therapeutic development, a clearer understanding of its biological actions across tissues and metabolic states will be essential [17].

Contributor Information

Anam Ahmad, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Michelle Shardell, Email: mshardell@som.umaryland.edu, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Rita Rastogi Kalyani, Department of Medicine, Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Richard D Semba, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Braxton D Mitchell, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Toshiko Tanaka, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.

Simeon Taylor, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Aman Shrestha, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Luigi Ferrucci, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.

Funding

This research was supported in part by National Institutes of Health (NIH) grants RF1 NS128360; R01 AG048069; R01 AG079854; and R01 HL111271. We also acknowledge the support of the University of Maryland, Baltimore Institute for Clinical and Translational Research (ICTR), which is funded in part by the National Center for Advancing Translational Sciences (NCATS) Clinical Translational Science Award (CTSA), UM1TR004926. This research was also supported in part by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH authors were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.

Disclosures

All authors have nothing to disclose.

Data availability

Restrictions apply to the availability of some or all data generated or analyzed during this study to preserve patient confidentiality or because they were used under license. The corresponding author will on request detail the restrictions and any conditions under which access to some data may be provided. InCHIANTI data can be accessed from the National Institute on Aging (https://www.nia.nih.gov/inchianti-study).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

Ahmad A, Shardell M, Kalyani R, et al. Supplemental Material for “Alpha-klotho, Insulin Resistance, and Adiposity Among Older Adults” by Ahmad A et al. Published online June 15, 2026. https://zenodo.org/records/20703660 [DOI] [PMC free article] [PubMed]

Data Availability Statement

[bvag140-B1] 1. Kuro-o M, Matsumura Y, Aizawa H, et al. Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature. 1997;390(6655):45‐51. [DOI] [PubMed] [Google Scholar]

[bvag140-B2] 2. Wang Y, Sun Z. Current understanding of klotho. Ageing Res Rev. 2009;8(1):43‐51. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bvag140-B3] 3. Matsumura Y, Aizawa H, Shiraki-Iida T, Nagai R, Kuro-o M, Nabeshima Y. Identification of the human klotho gene and its two transcripts encoding membrane and secreted klotho protein. Biochem Biophys Res Commun. 1998;242(3):626‐630. [DOI] [PubMed] [Google Scholar]

[bvag140-B4] 4. Xu Y, Sun Z. Molecular basis of Klotho: from gene to function in aging. Endocr Rev. 2015;36(2):174‐193. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bvag140-B5] 5. Kurosu H, Yamamoto M, Clark JD, et al. Suppression of aging in mice by the hormone Klotho. Science. 2005;309(5742):1829‐1833. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bvag140-B6] 6. Semba RD, Cappola AR, Sun K, et al. Plasma klotho and cardiovascular disease in adults. J Am Geriatr Soc. 2011;59(9):1596‐1601. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bvag140-B7] 7. Abraham CR, Li A. Aging-suppressor Klotho: prospects in diagnostics and therapeutics. Ageing Res Rev. 2022;82:101766. [DOI] [PubMed] [Google Scholar]

[bvag140-B8] 8. Semba RD, Cappola AR, Sun K, et al. Plasma klotho and mortality risk in older community-dwelling adults. J Gerontol A Biol Sci Med Sci. 2011;66(7):794‐800. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Alpha-klotho, insulin resistance, and adiposity among older adults

Anam Ahmad

Michelle Shardell

Rita Rastogi Kalyani

Richard D Semba

Braxton D Mitchell

Toshiko Tanaka

Simeon Taylor

Aman Shrestha

Luigi Ferrucci

Abstract

Context

Objective

Main outcome measures

Design, setting, and participants

Results

Conclusion

Materials and methods

Setting and participants

Figure 1.

Study measures

Biomarkers

Exposure

Outcomes

Other biomarkers

Other covariates

Statistical analysis

Results

Table 1.

Figure 2.

Table 2.

Table 3.

Table 4.

Discussion

Contributor Information

Funding

Disclosures

Data availability

References

Associated Data

Data Citations

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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